iiinova — where your digital self keeps evolving

Greg Faletto @gregoryfaletto.com · 24/05/2026

Finally, the package also supports {broom} functions like `tidy`, `glance`, and `augment` for easier interactions with other tidyverse packages.

Documentation snippet for the fetwfe package titled 'Tidy output with broom', from the blog post at https://gregoryfaletto.com/2026/05/23/now-on-cran-fetwfe-version-1-10-0/. It demonstrates tidyverse integration using tidy(), glance(), and augment() functions, featuring a console summary table for ATT and cohorts 2 through 4.

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Greg Faletto @gregoryfaletto.com · 24/05/2026

Cluster-robust standard errors are also now implemented. It is also worth noting that this update fixes errors in the previous package versions in the standard error calculations. This affects both point estimates and p-values.

Documentation snippet for the fetwfe package titled 'Experimental cluster-robust standard errors', from the blog post at https://gregoryfaletto.com/2026/05/23/now-on-cran-fetwfe-version-1-10-0/. It demonstrates using the se_type = 'cluster' option to calculate a unit-clustered sandwich estimator, complete with example R code and console output.

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Greg Faletto @gregoryfaletto.com · 24/05/2026

There are also expanded features for statistical inference. P values and confidence intervals are available for all estimated treatment effects, and there's a "selected" binary column indicating whether the cohort ATTs were estimated as exactly 0--which serves as an asymptotic 100% CI.

Documentation snippet for the fetwfe package titled 'Testing whether an effect is zero', from the blog post at https://gregoryfaletto.com/2026/05/23/now-on-cran-fetwfe-version-1-10-0/. It explains the 'selected' flag property using a console output table for 'result$catt_df' across Cohorts 2, 3, and 4.

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Greg Faletto @gregoryfaletto.com · 24/05/2026

What's new in version 1.10.0 compared to the previous CRAN release: first of all, the package now provides convenience functions for event study estimates and plots.

Blog post excerpt (from https://gregoryfaletto.com/2026/05/23/now-on-cran-fetwfe-version-1-10-0/) for the fetwfe package titled 'Event-study estimates and plots'. It describes the eventStudy() and plot() functions, including an example output table showing event-time estimates, standard errors, confidence intervals, and p-values.

Event-study estimates plot showing pooled ATT over event time (t−r) from 0 to 4. Black dots with vertical error bars indicate estimates and confidence intervals; a dashed horizontal line marks 0. All estimates are below zero: approximately −0.75 at time 0, −1.03 at time 1, −1.28 at time 2, −0.34 at time 3, and −0.84 at time 4. The most negative estimate occurs at time 2, while time 3 is closest to zero. Error bars are widest at times 1–2 and narrowest at time 3.

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Greg Faletto @gregoryfaletto.com · 24/05/2026

FETWFE is a middle ground. It regularizes the coefficients in the ETWFE regression towards each other, so that if e.g. within a cohort the treatment effect is the same in time periods t and t + 1, we only estimate one TE for those two time periods, preserving unbiasedness with better efficiency.

$Screenshot from Faletto, Gregory. "Fused extended two-way fixed effects for difference-in-differences with staggered adoptions." arXiv preprint arXiv:2312.05985 (2023). Figure 1: A line graph visualizing connections between estimated marginal average treatment effect terms (tau_rt) across time t (horizontal axis, 1 to 6) and cohorts r (vertical axis, 2 to 5). Details of the grid and connections are described in the accompanying text. Figure 1: Visualization of which of the estimated marginal average treatment effect terms \hat{\tau}_{rt} from regression (1.5) (which estimate the average treatment effects \tau_{ATT}(r,t) from Equation 1.1) we penalize towards each other in the FETWFE penalty (5.1). In this setting, T = 6 and R = {2, ..., 5}. The horizontal axis depicts time and the vertical axis depicts cohorts. FETWFE works well under an assumption that the linked treatment effects tend to be close together, and at least some of them are exactly equal. See further details in Section 5.$

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Greg Faletto @gregoryfaletto.com · 24/05/2026

Briefly, under staggered adoptions, the two-way fixed effects estimator is biased for the average treatment effect on the treated units. It turns out that estimating a single coefficient for the treatment effect on all cohorts at all times doesn't aggregate into a sensible estimate of the ATT.

Screenshot of an excerpt from Chapter 18 of: Hansen, Bruce. Econometrics. Princeton University Press, 2022.

"See Exercise 18.1.) Thus the above regression is identical to the two-way fixed effects regression Y_it = theta D_it + u_i + v_t + epsilon_it (18.3) where u_i is a restaurant fixed effect and v_t is a time fixed effect. The simplest method to implement this"

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Greg Faletto @gregoryfaletto.com · 14/05/2026

setting aside the war in Iran--a lot of smart folks twist themselves into a pretzel because they can't wrap their heads around 1/4 of americans believing Trump's promises that he was going to lower prices and being pissed that he didn't, because that's too dumb to believe. but that's the situation.

Line chart titled “Trump hits new approval low overall and on prices.” The chart shows Donald Trump’s net approval ratings from early 2025 through April 2026 across several issues, including inflation/cost of living, immigration, deportations, trade, health care, civil rights/democracy, and overall approval. All trend lines decline over time and remain below zero, indicating more disapproval than approval. The dark green “Inflation/cost of living” line falls the most sharply, dropping from about –3 in early 2025 to roughly –42 by April 2026, making it the lowest-rated issue. Other issue ratings cluster between about –10 and –25 near the end of the period. A tooltip highlights an inflation/cost of living rating of –26.1 on February 26, 2026. Source notes and methodology appear below the chart.

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Greg Faletto @gregoryfaletto.com · 04/05/2026

Yeah, people saying they’re happy with the product and it being profitable are two different things

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Greg Faletto @gregoryfaletto.com · 26/04/2026

Gonna throw a fit if I don't get 48x points

Screenshot of a shopping app screen showing “4 of 4 Coupons Clipped” at the top. Four offers are listed vertically:

* “Earn 4X Points” with Chevron/Texaco branding, marked “Clipped: Add 1 Total for Offer,” and a “Shop Deal” link.
* “Earn 3X Points” with Waterfront Bistro branding, also marked “Clipped: Add 1 Total for Offer,” with a “Shop Deal” link.
* “Earn 2X Points” with Signature Select branding, marked “Clipped: Add 1 Total for Offer,” with a “Shop Deal” link.
* “Earn 2X Points” with FreshPass Perk branding, marked “Clipped: Add 1 Total for Offer,” with a “Shop Deal” link.
Each offer includes a small tag icon on the right side.

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Greg Faletto @gregoryfaletto.com · 21/04/2026

POV: you’re a Trump administration official boarding a 3 AM flight to The Hague in zip ties on an aggressively-interpreted legal theory that you’re just going to have to contest from a cell in the Netherlands while the executive branch pretends to try to get you back to the US

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Greg Faletto @gregoryfaletto.com · 07/04/2026

“Simulated surveys” is basically this meme except the person says “Generate a sample from a population where the mean is 4” and then the computer says “>THE SAMPLE MEAN IS 4.03”

Simple black-and-white comic drawing of a person standing beside a desktop computer. The person says, “Say ‘I am alive’.” The computer monitor displays the text “> I AM ALIVE.” The person responds, “oh my god.”

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Greg Faletto @gregoryfaletto.com · 02/03/2026

This is interesting. First of all, I think one could argue that minimizing squared error (and therefore caring about the bias/variance tradeoff) satisfies this. It depends on what your metric for "like" is.

Quote from blog post with highlighting:

"I think a useful starting point in these discussion is to say what you want out of the published literature. I don’t have a full answer, but one thing that I would like is for published research coefficients to be unbiased. If a published result has an effect of 1, then I would like to (correctly) believe that if I were to somehow average across all (published and unpublished) research conducted on this topic with similar setups then I would get something like 1. Ideally, this would also be informative of future replications of the research too."

The highlighted portion is the sentence beginning “If a published result has an effect of 1 …” and ending “… then I would get something like 1.”

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Greg Faletto @gregoryfaletto.com · 27/02/2026

I was not prepared for the firestorm when I gave my take in the old place

$Excerpt from: Lehmann, Erich Leo, ed. Elements of large-sample theory. New York, NY: Springer New York, 1999. 4.1 Confidence intervals Rather than testing that a parameter ( \theta ) of interest has a specified value ( \theta_0 ) (or falls short of ( \theta_0 )), one will often want to estimate ( \theta ). Two approaches to the estimation problem are (i) estimation by confidence intervals with which we shall be concerned in the present section and (ii) point estimation which will be discussed in Section 2. Confidence intervals for ( \theta ) are random intervals [ \big(\underline{\theta}(X_1, \ldots, X_n), \overline{\theta}(X_1, \ldots, X_n)\big) ] 220 4. Estimation which have a guaranteed probability of containing the unknown parameter ( \theta ), i.e., for which [ P_\theta \big[ \underline{\theta}(X_1, \ldots, X_n) \le \theta \le \overline{\theta}(X_1, \ldots, X_n) \big] \ge \gamma \text{ for all } \theta ] for some preassigned confidence level ( \gamma ). Since excessively large coverage probability requires unnecessarily long intervals, we shall instead require that [ (4.1.1) \qquad \inf_\theta P_\theta \big[ \underline{\theta}(X_1, \ldots, X_n) \le \theta \le \overline{\theta}(X_1, \ldots, X_n) \big] = \gamma. ]$

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Greg Faletto @gregoryfaletto.com · 08/02/2026

It sort of feels like if you pay $8 million for a 30 second Super Bowl ad, they should throw in the rights to refer to it as the Super Bowl instead of the "big game"

“Promotional image with the text ‘Watch the Alexa+ big game ad’ overlaid on a close-up of a person’s face in a blue-toned scene.”

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Greg Faletto @gregoryfaletto.com · 05/01/2026

When is Jim Amendments joining Bluesky

Meme-style image from a movie scene. In a dark industrial setting, a muscular masked man stands with arms outstretched, blocking a rushing wall of water. Over his body is white text reading, ‘Soldiers with $693bn of financial backing and state of the art weapons from the Dept of Defense.’ To the right, a much smaller person in a bright pink bodysuit stands casually with arms open. Text next to them reads, ‘Me protecting my 650 sq ft second floor walk up studio from unconstitutional quartering.’

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Greg Faletto @gregoryfaletto.com · 05/01/2026

In the Democratic Party, the first step in the crisis management playbook is always, always to explain to this 40% of the public why Republicans' basic framing of the argument is correct and they're actually making some pretty good points

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Greg Faletto @gregoryfaletto.com · 25/12/2025

I'm reading this part of the structured abstract (I don't have access to the full text), and I see that they're talking about distributions where 9 of the top 10 highest adult performers weren't top 10 child performers. That seems significantly more lopsided than your plot.

Screenshot of a scholarly article page titled ‘ADVANCES.’ The page contains dense academic text discussing research on exceptional performance. A large middle paragraph is highlighted in light blue, emphasizing that early exceptional performers and later exceptional performers are usually different individuals. Examples note that top youth and adult performers in chess, education, and international athletics differ by nearly 90 percent over time.

Highlighted text: "Early exceptional performers and later exceptional performers within a domain are rarely the same individuals but are largely discrete populations over time. For example, world top-10 youth chess players and later world top-10 adult chess players are nearly 90% different individuals across time. Top secondary students and later top university students are also nearly 90% different people. Likewise, international-level youth athletes and later international-level adult athletes are nearly 90% different individuals."

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Greg Faletto @gregoryfaletto.com · 06/12/2025

It's crazy how many people think Biden failed to cancel any student loan debt. $190 billion! www.nasfaa.org/news-item/35...

**Alternative text:**
A screenshot of a news article titled “Biden Administration Announces ‘Final’ Student Loan Debt Relief Approvals.” The byline reads: “By Maria Carrasco, NASFAA Staff Reporter.” The article explains that shortly before the Trump administration takes office, the Biden administration announced its final approvals of student loan forgiveness through the Income-Based Repayment (IBR) plan and borrower-defense claims. It states that the Department of Education approved $600 million in forgiveness for 4,550 borrowers under IBR and additional forgiveness for 4,100 former DeVry University students, with no total amount specified for that portion. The article concludes by noting that the administration has approved $188.8 billion in forgiveness for 5.3 million borrowers since taking office.

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Greg Faletto @gregoryfaletto.com · 30/11/2025

Total inference cost is [cost per token] times [tokens used]. AI companies want the first number to go down (ideally a lot) and the second number to go up (ideally a lot). So total inference cost isn't a great metric. First number is in fact going down dramatically (the y-axis is on a log scale).

Source: https://hai.stanford.edu/ai-index/2025-ai-index-report/research-and-development

A line chart shows the steep decline in inference prices for various large language models from 2022 to 2025. The vertical axis uses a log scale for price per million tokens, and the horizontal axis shows publication dates. Multiple colored lines represent benchmarks such as GPT-3.5-level MMLU, GPT-4-level HumanEval, PhD-level GPQA, and LMSYS Chatbot Arena Elo. All lines slope sharply downward over time. Models labeled GPT-3.5, GPT-4-0314, GPT-4o-2024-05, Claude-3.5-Sonnet-2024-06, Llama-3.1-Instruct-8B, DeepSeek-V3, Phi 4, and Gemini-1.5-Flash-8B appear along the trend. The chart illustrates dramatic cost reductions, from double-digit dollars per million tokens in 2022 to well under one dollar by late 2024.

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Greg Faletto @gregoryfaletto.com · 24/11/2025

Most people make at least some conscious choices at points in their life in the hopes of eventually getting a more desirable job, like education choices. "It's hard to do that," "some people are born with advantages," and "there are wide disparities in wages" are all things I would have agreed with

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Greg Faletto @gregoryfaletto.com · 20/11/2025

doesn't really matter if you're dead. Uber reports 153 people died in Ubers in 2021 - 2022 www.uber.com/us/en/about/...

A data table titled “2017–2022 motor vehicle fatality data, Uber-related and United States” compares Uber-related fatality metrics with national metrics across three two-year periods: 2017–2018, 2019–2020, and 2021–2022. Columns list: Year; number of Uber-related fatalities; frequency of fatalities by number of trips; percentage of trips involving fatalities; Uber’s fatality rate per 100 million vehicle miles traveled; the national fatality rate per 100 million miles; and the rate change from the prior period for both Uber and the national figures. Values show Uber fatalities of 107, 101, and 153 in the three periods, with fatality frequencies ranging from roughly 1 in 22 million to 1 in 12 million trips. Uber’s fatality rate increases from 0.58 to 0.62 to 0.87 across periods, while national rates rise from 1.15 to 1.22 to 1.35. Rate-change columns show increases for later periods (e.g., +7% and +40% for Uber; +6% and +11% nationally), with dashes for the baseline period.

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Greg Faletto @gregoryfaletto.com · 12/11/2025

Maybe, but this table wouldn't tell you either way

Meme of rapper Drake expressing preferences. In the top panel, Drake turns away with a disgusted expression, rejecting the label “DIFFERENCE-IN-DIFFERENCES.” In the bottom panel, he smiles and points approvingly at the label “DIFFERENCES.” The meme humorously suggests a preference for simple differences over the statistical method “difference-in-differences.”

(it's a sarcastic meme, this table is just showing differences from a handful of cities. At the very least you'd want to look at difference-in-differences over a larger set of cities, and even that would be considered pretty crude by contemporary research standards.)

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Greg Faletto @gregoryfaletto.com · 29/10/2025

the world if non-VC-and-C-suite technology brothers, sisters, and non-binary siblings ran everything

The image depicts a futuristic cityscape with sleek, modern architecture and advanced technology. Tall glass skyscrapers with curved designs rise in the background alongside domed and geometric structures made of reflective materials. Numerous flying vehicles move through the sky, while streamlined trains glide on elevated tracks. In the foreground, a person walks along a landscaped green area near a reflective pool, accompanied by a small robotic dog. The scene is bright and sunny, with a clean, high-tech atmosphere suggesting an advanced, utopian future.

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Greg Faletto @gregoryfaletto.com · 26/10/2025

Or (*cough*) maybe you believe *some* TEs are not unique (e.g. 2 cohorts starting treatment consecutively have the same initial TE) & want to use this to reduce variance, but rather than picking the structure yourself & risking bias you want to use ML to learn it from data arxiv.org/abs/2312.05985

Figure 1 in Faletto (2025) (https://arxiv.org/abs/2312.05985). The image shows a triangular grid plotted on a coordinate system, where the horizontal axis (labeled *t*) represents time and the vertical axis (labeled *r*) represents cohorts. The grid displays points connected by lines, forming a right triangle aligned along the diagonal from the lower left to the upper right. Each point is labeled with a symbol of the form **τ̂<sub>rt</sub>**, representing estimated marginal average treatment effect terms.

The points appear as follows:

* Along *r = 2*: τ̂₂₂, τ̂₂₃, τ̂₂₄, τ̂₂₅, τ̂₂₆
* Along *r = 3*: τ̂₃₃, τ̂₃₄, τ̂₃₅, τ̂₃₆
* Along *r = 4*: τ̂₄₄, τ̂₄₅, τ̂₄₆
* Along *r = 5*: τ̂₅₅, τ̂₅₆

Each horizontal line connects treatment effects for a given cohort across time, while the diagonal connects τ̂₂₂, τ̂₃₃, τ̂₄₄, and τ̂₅₅.

The figure caption explains that this visualization illustrates which estimated treatment effect terms τ̂<sub>rt</sub> are penalized toward each other in the FETWFE model, where *T = 6* and *R = {2,…,5}*. The method assumes that linked treatment effects are close together or equal.

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Greg Faletto @gregoryfaletto.com · 26/10/2025

The short explanation is: DID assumes "parallel trends": a time-invariant selection bias in outcomes between eventually-treated and never-treated units. So you can use pre-treatment observations to estimate the selection bias. (Here β1 is selection bias, β2 is the "seasonality effect", β3 is TE.)

Difference-in-differences diagram. Pre-intervention means: treatment 50 (β₀+β₁), comparison 35 (β₀). Post-intervention: comparison 55 (β₀+β₂); treatment observed 85 (β₀+β₁+β₂+β₃). A dashed green line shows the treatment counterfactual at 70 (β₀+β₁+β₂). Gray lines connect pre to post for observed trends; x-axis labeled “Pre-intervention” and “Post-intervention.”

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Greg Faletto @gregoryfaletto.com · 18/10/2025

This later paper by a similar group was an easier entry point for me and also lays out this specific topic more clearly than the 2019 one IMO

The image shows the title and citation information from the top of an academic journal article.

Text reads:
**JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS**
**2021, VOL. 30, NO. 2, 503–517**
*[https://doi.org/10.1080/10618600.2020.1831930](https://doi.org/10.1080/10618600.2020.1831930)*

Below that, the article title appears in large bold blue text:
**Local Linear Forests**

The author line lists:
Rina Friedberg¹, Julie Tibshirani², Susan Athey³, and Stefan Wager³ —
with superscript letters **a**, **b**, and **c** in blue following the names.

$The image shows a section of an academic paper titled **“2. Local Linear Forests”** in bold blue text. The passage explains that local linear forests use random forests to generate weights, which can act as kernels for local linear regression. It defines training data ((X_1, Y_1), \ldots, (X_n, Y_n)) with (Y_i = \mu(X_i) + \epsilon_i), and discusses using a random forest to estimate the conditional mean function (\mu(x_0) = \mathbb{E}[Y \mid X = x_0]) at a test point (x_0). The text describes random forests as ensemble methods that average tree predictions. For each tree (T_b) in a forest of (B) trees, the leaf (L_b(x_0)) has a predicted response (\hat{\mu}*b(x_0)), defined as the average response of all training data points assigned to that leaf. The overall prediction is given by [ \hat{\mu}(x_0) = (1/B) \sum*{b=1}^B \hat{\mu}_b(x_0). ]$ $The image shows a section of an academic paper containing text and mathematical equations about random forests viewed as adaptive weight generators. The paragraph cites *Hothorn et al. (2004)*, *Meinshausen (2006)*, and *Athey, Tibshirani, and Wager (2019)* — all references appear in blue. The equations express how the random forest prediction (\hat{\mu}(x_0)) can be written as weighted averages: [ \hat{\mu}(x_0) = \frac{1}{B} \sum_{b=1}^B \sum_{i=1}^n Y_i \frac{1{X_i \in L_b(x_0)}}{|L_b(x_0)|} = \sum_{i=1}^n Y_i \frac{1}{B} \sum_{b=1}^B \frac{1{X_i \in L_b(x_0)}}{|L_b(x_0)|} = \sum_{i=1}^n \alpha_i(x_0) Y_i, ] where the forest weight (\alpha_i(x_0)) is defined as [ \alpha_i(x_0) = \frac{1}{B} \sum_{b=1}^B \frac{1{X_i \in L_b(x_0)}}{|L_b(x_0)|}. ] Equation (4) appears on the right side of the last expression. The text notes that for each (i), (0 \le \alpha_i(x_0) \le 1), and the weights sum to 1 if at least one tree includes a nonempty cell containing (x_0).$

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Greg Faletto @gregoryfaletto.com · 18/10/2025

Wait, what exactly is this paper adding that isn't in Athey et al (2019)? (They cite it but don't really spell out the novelty compared to that paper explicitly)

“To avoid this issue, we cast forests as a type of adaptive locally weighted estimators that first use a forest to calculate a weighted set of neighbors for each test point
𝑥
x, and then solve a plug-in version of the estimating equation (1) using these neighbors. Section 2.1 gives a detailed treatment of this perspective.”

$The image shows a section of a research paper discussing forest-based algorithms for learning adaptive, problem-specific weights. The text cites *Hothorn et al. (2004)* and *Meinshausen (2006)*, both in blue, and presents a mathematical definition for weights (\alpha_i(x)). The equations shown are: [ \alpha_{bi}(x) = \frac{1({X_i \in L_b(x)})}{|L_b(x)|}, \quad \alpha_i(x) = \frac{1}{B} \sum_{b=1}^B \alpha_{bi}(x). ] The text explains that a set of (B) trees is grown, each with leaves (L_b(x)) representing training examples that share a leaf with (x). These weights sum to 1 and define the forest-based adaptive neighborhood of (x).$ The image shows a line of text from an academic paper. It reads:
“Finally, for the special case of regression trees, our weighting-based definition of a random forest is equivalent to the standard ‘average of trees’ perspective taken in Breiman (2001): If we estimate the conditional mean function.”

The image shows a line of text from an academic paper. It reads:
“Finally, for the special case of regression trees, our weighting-based definition of a random forest is equivalent to the standard ‘average of trees’ perspective taken in Breiman (2001): If we estimate the conditional mean function.”

$The image shows a mathematical passage from an academic paper defining the conditional mean function for regression trees. It reads: [ \mu(x) = \mathbb{E}[Y_i \mid X_i = x], ] as identified in (1) using [ \psi_{\mu(x)}(Y_i) = Y_i - \mu(x), ] then we see that [ \sum_{i=1}^n \frac{1}{B} \sum_{b=1}^B \alpha_{bi}(x)(Y_i - \hat{\mu}(x)) = 0 ] if and only if [ \hat{\mu}(x) = \frac{1}{B} \sum_{b=1}^B \hat{\mu}_b(x), ] where [ \hat{\mu}*b(x) = \sum*{{i : X_i \in L_b(x)}} Y_i / |L_b(x)| ] is the prediction made by a single CART regression tree.$

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Greg Faletto @gregoryfaletto.com · 01/10/2025

Yeah it really sucked when Biden didn't get his way on the child tax credit. en.wikipedia.org/wiki/Build_B...

Screenshot of a Wikipedia article section discussing Senator Joe Manchin’s opposition to extending the child tax credit. The text explains that Senate Democrats supported the enhanced credit, citing reductions in child poverty and food insecurity. Manchin demanded stricter work requirements and criticized reliance on temporary funding. He rejected claims that he wanted to eliminate the credit, calling them false, but expressed concerns about misuse of funds. The Census Bureau is cited as reporting that most families used the payments for essentials like food, clothing, utilities, and education. Numerous reference citations in blue are interspersed throughout the text.

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Greg Faletto @gregoryfaletto.com · 25/08/2025

It's that "age as a predictor" plot from like a month ago! Looks great!

A man sitting in a chair with his legs stretched out, holding a drink can and a cigarette in one hand while pointing with the other. He appears to be reacting strongly to something off-screen.

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Greg Faletto @gregoryfaletto.com · 24/08/2025

This isn't thought through IMO. Author's argument is *in favor of* exposing oneself to those kinds of posts. It only makes sense if being influenced by writing is something that only happens to other people--not the reader (who she's trying to influence with her writing), and certainly not *her*.

A screenshot of a Twitter post embedded in an article. The article text above the post discusses persuasion, noting that persuadable people are often bystanders who rarely participate in online debates but may be influenced by what they see. It asks whether readers will leave such bystanders exposed only to certain arguments.

The Twitter post is from an account called *The Transformed Wife (@godlywomanhood)* and reads: “Men’s role is to provide. Women’s role is to nurture. Our society has lost SO much (especially the children) because most women no longer nurture. They would rather provide. 😟”

The post shows engagement metrics: 63 comments, 67 retweets, 210 likes, 6 bookmarks, and a share icon.

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Greg Faletto @gregoryfaletto.com · 18/08/2025

No biggie, but feel free to use it, and if you like it leave a review!

Screenshot of the ChatGPT interface showing the "Alt Text Writer 5" GPT menu open. The dropdown menu lists options including Model, New chat, About, Edit GPT, Hide from sidebar, Copy link, Review GPT, and Report GPT. The "Review GPT" selection is highlighted. On the right, the Alt Text Writer page displays its name, creator "Gregory M Faletto," and description explaining it generates W3C-compliant alt text when an image is uploaded.

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Greg Faletto @gregoryfaletto.com · 09/08/2025

Um I didn't check all the details and some of the photos are obviously off, but I think GPT 5 did pretty well. (I'm in the habit of checking these kinds of posts because usually they don't replicate on near-SOTA--seem to typically either be based on free or outdated models)

A grid of official portrait photographs of U.S. presidents from 1929 to the present, each labeled with name and years in office. Top row: Herbert Hoover (1929–1933), Franklin D. Roosevelt (1933–1945), Harry S. Truman (1945–1953), Dwight D. Eisenhower (1953–1961). Second row: John F. Kennedy (1961–1963), Lyndon B. Johnson (1963–1969), Richard Nixon (1969–1974), Gerald R. Ford (1974–1977). Third row: Jimmy Carter (1977–1981), Ronald Reagan (1981–1989), George H. W. Bush (1989–1993), Bill Clinton (1993–2001). Bottom row: George W. Bush (2001–2009), Barack Obama (2009–2017), Donald Trump (2017–2021), Joe Biden (2021–present).

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Greg Faletto @gregoryfaletto.com · 07/08/2025

Darn, hasn't propagated to my Plus account yet on web or mobile 😔

A cartoon scene showing Squidward from behind as he peers through six horizontal window blinds. Outside, SpongeBob SquarePants and Patrick Star stand on a light-colored surface, arms raised and mid-jump, as if dancing or cheering.

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Greg Faletto @gregoryfaletto.com · 07/08/2025

...so when do I get to try GPT-5? 😥

A dark-mode screenshot of the ChatGPT model-selection dropdown. At the top it reads “ChatGPT o3” with a downward arrow. Under a “Models” heading are four options:

GPT-4o – Great for most tasks

o3 – Uses advanced reasoning (checked)

o4-mini – Fastest at advanced reasoning

o4-mini-high – Great at coding and visual reasoning

Below them is an “Other models >” button, which is expanded to reveal two more entries:

GPT-4.1 – Great for quick coding and analysis

GPT-4.1-mini – Faster for everyday tasks

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Greg Faletto @gregoryfaletto.com · 05/07/2025

Finally, there's now an S3 class for the output of fetwfe(), which works with functions like print() and summary(). You'll notice the output looks nicer now. See, for example, the getting started vignette cran.r-project.org/web/packages...

$Screenshot of R code and console output showing the use of the `fetwfe` package to estimate a Fused Extended Two-Way Fixed Effects model on panel data and print a summary that includes an overall average treatment effect (ATT), cohort-specific treatment effects (CATT) for three cohorts, and model details (number of units, time periods, treated cohorts, covariates, features, selected size, and tuning parameter). --- The image is divided into two main sections: 1. **R code:** * `library(fetwfe)` * A call to `fetwfe()` with arguments: * `pdata = pdata` (panel dataset) * `time_var = "time"` (time variable) * `unit_var = "unit"` (unit identifier) * `treatment = "treated"` (treatment dummy indicator) * `response = "response"` (response variable) * A call to `summary(result)` to display the estimation results. 2. **Console output (Summary of Fused Extended Two-Way Fixed Effects):** * **Overall ATT:** 29.4540 (SE = 5.6503, 95% CI = \[18.3796, 40.5283]) * **CATT (preview) table:** * Cohort 11: Estimated TE = 13.93837, SE = 0.50308, 95% CI = \[12.95236, 14.92438] * Cohort 12: Estimated TE = 19.65173, SE = 0.41869, 95% CI = \[18.83111, 20.47235] * Cohort 7: Estimated TE = 56.13475, SE = 0.37109, 95% CI = \[55.40744, 56.86207] * **Model Details:** * Units (N): 30 * Time periods (T): 20 * Treated cohorts (R): 3 * Covariates (d): 0 * Features (p): 55 * Selected size: 17 * Lambda\*: 0.8412 This image illustrates both the code needed to run the estimator and the key numerical results of the fixed-effects analysis.$

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Greg Faletto @gregoryfaletto.com · 05/07/2025

Second, if you want to compare these estimators, there's now a set of functions that make it easy to conduct simulation studies. Check out the new vignette for details. cran.r-project.org/web/packages...

$An image showing a screenshot of R code in a monospaced font. The code reads: ``` coefs <- genCoefs(R = 3, T = 4, d = 2, density = 0.1, eff_size = 2, seed = 2025) result_piped <- coefs |> simulateData(N = 60, sig_eps_sq = 1, sig_eps_c_sq = 1) |> fetwfeWithSimulatedData() cat("Estimated Overall ATT from piped workflow:", result_piped$att_hat, "\n") #> Estimated Overall ATT from piped workflow: -0.7045089 true_tes_piped <- coefs |> getTes() # Print the true overall treatment effect cat("True Overall ATT:", true_tes_piped$att_true, "\n") #> True Overall ATT: -0.6666667 ``` The code first generates coefficient values with `genCoefs`, then pipes them through `simulateData` and `fetwfeWithSimulatedData`, printing an estimated average treatment‐on‐the‐treated (ATT) of approximately –0.7045. It then uses `getTes()` on the same coefficients to retrieve and print the true ATT of –0.6667.$

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Greg Faletto @gregoryfaletto.com · 05/07/2025

First of all, I added an implementation of extended two-way fixed effects (@jmwooldridge.bsky.social 2021), etwfe(), with inputs and outputs aligned with fetwfe(). There's also now betwfe(), which implements a bridge-penalized (includes lasso and ridge regression) version of etwfe().

$A screenshot of an R console showing code and output for an extended two-way fixed effects analysis. The code loads the `fetwfe` and `did` packages, loads the `mpdta` dataset, and transforms it into `pdata` with `attgtToFetwfeDf`, specifying outcome `lemp`, time variable `year`, unit identifier `countyreal`, treatment onset `first.treat`, and covariate `lpop`. Then it runs `etwfe(pdata, time_var="time_var", unit_var="unit_var", treatment="treatment", response="response", covs="lpop")`. Below is the “Extended Two-Way Fixed Effects Results”: * **Overall Average Treatment Effect (ATT):** – Estimate: –0.0452 – Std. Error: 0.0145 – 95 % CI: \[–0.0736, –0.0167] * **Cohort Average Treatment Effects (CATT):** | Cohort | Estimated TE | SE | 95 % CI low | 95 % CI high | | ------ | ------------ | ---------- | ----------- | ------------ | | 2004 | –0.08762696 | 0.03555885 | –0.15732102 | –0.01793290 | | 2006 | –0.02127833 | 0.02128938 | –0.06300475 | 0.02044809 | | 2007 | –0.04595453 | 0.01633327 | –0.07796715 | –0.01394190 | * **Model Details:** – Units (N): 500 – Time periods (T): 5 – Treated cohorts (R): 3 – Covariates (d): 1 – Features (p): 29$

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Greg Faletto @gregoryfaletto.com · 28/06/2025

this rules. Haven't taken the plunge to pay for Gemini access, been making the most of my free daily 2.5 Pro prompts for math. The CLI preview access is a super helpful workaround. I just put my prompt in a text file along with the pdf I had uploaded in a question I had asked in the web interface.

Screenshot of a dark-themed macOS Terminal window running “Gemini.” Across the top, large ASCII-art letters spell “GEMINI” in a pastel gradient from blue (left) through purple to pink (right). Beneath, white text on black lists four “Tips for getting started,” such as asking questions, being specific, creating a GEMINI.md file, and using /help. The prompt then shows a user command instructing Gemini to load a PDF on Oracle efficient variable selection and a question.txt file for feedback. A bordered status box follows, with a green checkmark and the message “ReadManyFiles Will attempt to read and concatenate files using patterns…,” indicating the files are being processed from the user’s Documents directory.

A terminal window displaying feedback on a mathematical proof. The text comments on the generalization of Theorem 1 from Kock (2013), noting that the new assumptions are reasonable within cited literature. It includes a detailed breakdown under the heading "Proof Structure and Logic." Step 1 discusses bounding an error term and using assumptions to eliminate a cross-term, and confirms the validity of the argument. Step 2 begins with a comment on a modification of Kock's Lemma 1. The content includes inline LaTeX code for mathematical notation.

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Greg Faletto @gregoryfaletto.com · 18/06/2025

Wow, yeah this appears to be another correct answer

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Greg Faletto @gregoryfaletto.com · 14/06/2025

Well here's one example of what people are talking about when they say HARKing is bad

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Greg Faletto @gregoryfaletto.com · 05/06/2025

I wish we'd acknowledge more that the biggest "losers" in the 2022 - 2024 economy was about the 70th - 90th percentile of income, who experienced rapid wage growth in the bottom quartile of earners as price increases. (chart is unfairly negative IMO since includes 2020, but couldn't find better one)

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