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Aleksander Molak @alxndrmlk.bsky.social · 29/09/2026

Our new episode with Robert J. Reynolds is out now! 1/ #CausalSky #StatSky #EpiSky #MLSky

Square Causal Bandits Podcast thumbnail on a blue background. Large white text reads 'CAUSAL MODELS at NASA', with NASA in red. Alex Molak is on the left and Robert J. Reynolds on the right, with a grey astronaut figure between them. A caption at the bottom reads 'Robert Reynolds, PhD'.

Aleksander Molak @alxndrmlk.bsky.social · 28/09/2026

Last week I went to New York to talk to Robert J. Reynolds, PhD, for a new podcast series with @sobor.bsky.social and UBC. Rob had quite a story to tell about his work with causal models at NASA. #CausalSky #StatSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 27/09/2026

We just sent today's issue of Causal Python Bi-Weekly. Here's what's inside: 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Causal Python Bi-Weekly issue 180 thumbnail. Headline 'When the best recommendation is the one we hold back' with a portrait of Athanasios Vlontzos (Hologen, ex-Spotify), on making Spotify's recommender causal using holdback data already collected. Also inside: Hume, de Finetti and induction, with David Rohde on deferring to a machine, and UBC's causal AI cluster launching October 8 with a keynote by Robert J. Reynolds.

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Aleksander Molak @alxndrmlk.bsky.social · 21/09/2026

I'm in NYC to record a couple of new episodes of the #CausalBanditsPodcast What questions should I ask Elias Bareinboim and Robert J. Reynolds? 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Alex smiling in a selfie in Times Square, New York City, wearing a beige corduroy jacket over a yellow polo shirt, with blurred billboards, skyscrapers, and crowds in the background.

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Aleksander Molak @alxndrmlk.bsky.social · 16/09/2026

AI assistants can convince us to make bad decisions while we keep believing they were helpful. And it's a significant problem in statistics, causal analysis, and research. 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Screenshot of Valerio Capraro's LinkedIn post: 'New PNAS paper shows that AI can manipulate your preferences, and you may still think it's helping you.' Below it, the PNAS paper header 'Human preferences are susceptible to covertly misaligned AI advice' by Sahand Sabour et al., and a side-eyeing Doge meme.

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Aleksander Molak @alxndrmlk.bsky.social · 13/09/2026

Peeking at A/B tests? Does the stopping rule actually matter? In today's issue of Causal Python Weekly: 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Causal Python Weekly issue thumbnail. Editorial headline 'Spotify is not using Bayesian A/B testing' by David Rohde (Criteo), on stopping rules, peeking, and what a confidence interval buys us. Also inside: UBC's Causal AI cluster and a new podcast series, plus new causal jobs.

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Aleksander Molak @alxndrmlk.bsky.social · 21/08/2026

In a few days, Yale University Press releases Scott Cunningham's Causal Inference: the Remix. But you can already read it for free online. 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Cover of Causal Inference: The Remix by Scott Cunningham on a green background, showing a clear cassette tape labelled "THE REMIX" in handwriting with loose tape unspooling across the cover. A red diagonal ribbon in the top left corner reads NEW.

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Aleksander Molak @alxndrmlk.bsky.social · 19/08/2026

The idea behind difference-in-differences is ingenious. But... Let's start from the beginning. #CausalSky #StatSky #EconSky #EpiSky #MLSky

A screenshot of a Bluesky post by Guido Imbens (@imbens.bsky.social) reading "Don't Do Difference In Differences (DDDID), cheers, guido", dated March 21, 2026, shown over a dark starry night sky background.

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Aleksander Molak @alxndrmlk.bsky.social · 17/08/2026

Continuous treatment difference-in-differences with debiased machine learning in Python? Why not?

New Package Alert graphic for did_multiplegt_stat: a Python implementation of the did_multiplegt_stat Stata package (pypi v0.1.2, Python 3.10 to 3.13, GPLv3) providing heterogeneity-robust difference-in-differences estimators with stayers for binary, discrete, or continuous treatments. Shows the install command: pip install did-multiplegt-stat.

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Aleksander Molak @alxndrmlk.bsky.social · 16/08/2026

When you rebuild a randomized trial from observational data, how often do you actually get the same answer? In this week's issue of Causal Python Weekly: 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Causal Python Weekly thumbnail. Left: 'Robust DiD in Python' with a photo of Clément de Chaisemartin and the tags heterogeneity robustness, continuous treatment, and debiased machine learning. Center: David Rohde, 'When heterogeneous effects are not optional'. Right: 'Can we trust emulated trials?' with a photo of Canlong Wang.

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Aleksander Molak @alxndrmlk.bsky.social · 14/08/2026

Negative controls are an everyday staple in epidemiology. This framework based on them is a secret in industry. 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Title page of the notebook 'Proximal Two-Stage Least Squares: causal inference with proxy variables when a confounder is unmeasured', part of the CausalPython series, shown above the first page of the paper 'An Introduction to Proximal Causal Learning' by Tchetgen Tchetgen, Ying, Cui, Shi and Miao.

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Aleksander Molak @alxndrmlk.bsky.social · 10/08/2026

Evidence-Based Decision-Making & Causality What do you want to learn next? Over the last few months, I've been asking people in our community what would be the most useful resources for them when it comes to their learning goals. 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 03/08/2026

Time to reveal our special guest! Today, we spent a great day on the Massachusetts Institute of Technology campus with Justin Bélair Tomorrow, at the Thomas Menino Center, we'll be joined by Professor Mark van der Laan 1/ #StatSky #CausalSKy #MLSky #EpiSky #EconSky

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Aleksander Molak @alxndrmlk.bsky.social · 02/08/2026

In Boston? Join us tomorrow for a session on causality and complex dynamic systems! Complex systems have a peculiar property: their emergent behaviors can act back on the lower-level entities that produce them, while also impacting emergent properties on arbitrary levels. 1/ #CausalSky #StatSky

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Aleksander Molak @alxndrmlk.bsky.social · 31/07/2026

I always felt that Python has a significant gap when it comes to experimental work. Sure, SciPy handles simple designs, power, or sample size for a t-test. But more complex designs? Those almost always required custom code. 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Slide titled 'Working with Clustered Experiments in Python' over a dark code screenshot: a Python snippet importing AnalysisPlan from cluster_experiments and building an analysis plan from a metrics dict, with analysis_type set to 'clustered_ols' and cluster_cols set to ['store_id'].

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Aleksander Molak @alxndrmlk.bsky.social · 10/07/2026

A Tool Is Just a Tool. And Isaac Gerber took it to another level. On Monday, we're releasing the final episode of Season 2 of #CausalBanditsPodcast. And it's going to be quite a finale! 1/ #CausalSky #StatSky #EpiSky #EconSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 06/07/2026

Can an LLM stand in for the humans in your A/B test? Mårten Schultzberg, Joel Persson, and Sebastian Ankargren take the question seriously and work out what it would actually require.

Title slide of the paper 'Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference' by Joel Persson, Marten Schultzberg, and Sebastian Ankargren (Spotify), above two density plots (Linear and Nonlinear DGP) showing Raw LLM ATE estimates biased away from the true effect while Calibrated ATE estimates center near it.

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Aleksander Molak @alxndrmlk.bsky.social · 21/06/2026

How much value is your experimentation program leaving on the table? This week in Causal Python Weekly: 1/ #CausalSky #StatSky #EconSky #EpiSky #MLSky

Causal Python Weekly thumbnail: Benchmarking Geo-Experiment Tools and the Value of Evidence-Based Decisions, featuring Robson Tigre and Alberto Abadie, plus the Causal Summit 2026.

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Aleksander Molak @alxndrmlk.bsky.social · 15/06/2026

How PC-Based Methods Err (and How to Detect it) Causal discovery methods built on the PC algorithm are sound, but only if every structural assumption holds and every conditional independence test is correct. 1/ #CausalSky #EpiSky #StatSky #MLSky #EconSky

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Aleksander Molak @alxndrmlk.bsky.social · 14/06/2026

What Does It Take to Trust a Causal Discovery Result? In this week's issue of Causal Python Weekly: 1/ #CausalSky #StatSky #EpiSky #EconSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 12/06/2026

In these personally challenging times, it feels nice to know that what we've been doing for the last 2+ years with the podcast brings value to the community. I just got a message from YouTube that Causal Bandits Podcast hit 100k views. Thank you for your continuing support! ❤️ #CausalSky #StatSky

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Aleksander Molak @alxndrmlk.bsky.social · 03/06/2026

Our standard predictive machinery requires statistical repetition. Can we say anything about the effects of one-time or first-time events? Alexander's model correctly predicted the behavior of oil prices in response to the events in the Strait of Hormuz 1/ #CausalSky #EconSky #MLSky #StatSky

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Aleksander Molak @alxndrmlk.bsky.social · 02/06/2026

"History doesn't repeat itself, but it rhymes." The new episode of the Causal Bandits Podcast with Alexander Denev is out now The recent closure and reopening of the Strait of Hormuz are unique events. 1/ #CausalSky #EconSky #EpiSky #MLSky #StatSky #FinTech

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Aleksander Molak @alxndrmlk.bsky.social · 31/05/2026

Predicting the Effects of One-Time Events, a DML Case Study, and More... Tomorrow, we premiere a brand-new conversation with Alexander Denev, meanwhile... ...in today's issue of Causal Python Weekly: 1/ #CausalSky #StatSky #MLSky #EconSKy #EpiSKy

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Aleksander Molak @alxndrmlk.bsky.social · 25/05/2026

Is Causal Inference Dead in the Age of Vibe Coding? This week at Allegro Tech Talks, I'll discuss 5 causal ideas that I believe we should be aware of in the age of vibes. We'll discuss: 1/ #CausalSky #StatSky #EpiSky #EconSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 24/05/2026

Causal sensitivity analysis is expensive at scale. Could a foundation model fix that? In this week's issue of Causal Python Weekly: 1/ #CausalSky #EconSky #EpiSky #StatSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 17/05/2026

Synthetic control is extremely popular. But are the popular recommendations on how to use it actually valid? In this week's issue of Causal Python Weekly: 1/ #CausalSky #StatSky #EpiSky #EconSky

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Aleksander Molak @alxndrmlk.bsky.social · 11/05/2026

In recent weeks, I was almost not present here. My mom got diagnosed with cancer and I was focusing on supporting her and making sure we get the best possible care for her. 1/

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Aleksander Molak @alxndrmlk.bsky.social · 03/05/2026

Most businesses have a confounding problem. Are digitalized ones safe? In this week's issue of Causal Python Weekly: - D. Rohde on whether fully-digital businesses are structurally protected from unobserved confounding (+what that means for everyone else) 1/ #CausalSky #StatSky #EpiSky #EconSky

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Aleksander Molak @alxndrmlk.bsky.social · 19/04/2026

The data only shows what we observed. What about everything else? In this week's issue of Causal Python Weekly: 1/ #CausalSky #StatSky #EpiSky #EconSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 11/04/2026

Significant results. Should we move forward? There's a fundamental asymmetry in how we react to "good" and "bad" results and it might affect how we make decisions. 1/ #CausalSky #EconSky #EpiSky #StatSky #ABTesting

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Aleksander Molak @alxndrmlk.bsky.social · 10/04/2026

The Post on Log-Transforms Last Week Generated Many Insightful Discussions. I don't agree with all the points, but I love the discussion! Most of my favorite comments emphasize the same thing (either implicitly or explicitly): ... 1/ #CausalSky #StatSky #EpiSky #EconSky

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Aleksander Molak @alxndrmlk.bsky.social · 06/04/2026

Reverse Bayes: Imagine you run a randomized controlled trial; The results come with a p-value of 0.03 -- significant at the customary level of 0.05. But are they credible? 1/ #CausalSky #EpiSky #StatSky #EconSky #ABTesting

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Aleksander Molak @alxndrmlk.bsky.social · 06/04/2026

Is Half of All Advertising Spend Wasted? In this week's issue of Causal Python Weekly: - David Rohde on why even granular digital data often can't tell you which half of your ad budget is working 1/ #CausalSky #EconSky #EpiSky #StatSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 02/04/2026

When Designing a Quasi-Experiment Can We Do Better Than a Genetic Algorithm? Lawrence De Geest on efficient designs: vist.ly/4w27a

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Aleksander Molak @alxndrmlk.bsky.social · 01/04/2026

"We intervene and the intervention changes the market dynamics" Two-sided marketplaces can be a challenging setting for impact measurement. In the new episode of the Causal Bandits Podcast, we meet Lawrence De Geest (Zoox, ex-Lyft, ex-NBA), a former... 1/ #CausalSky #StatSky #EpiSky #EconSky

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Aleksander Molak @alxndrmlk.bsky.social · 31/03/2026

We Often Think About Randomized Trials as "the Gold Standard" for Causal Inference. And yet we almost as often forget to ask what information they provide and under what circumstances. 1/ #CausalSky #EconSky #EpiSky #StatSky #MLSky #ABTesting

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Aleksander Molak @alxndrmlk.bsky.social · 30/03/2026

"Data Not Normal? Just Log-Transform it." You can find it in textbooks, StackOverflow, and hear it from your colleague. And it's not always wrong, but it's way more dangerous than we tend to realize. 1/ #StatSky #Biostats #Biotech

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Aleksander Molak @alxndrmlk.bsky.social · 29/03/2026

What If We Reversed Bayes to Test How Robust Our Findings Are to Skepticism? In this week's issue of Causal Python Weekly: - Lu Qian's new blog post on Reverse Bayes and evidence 1/ #CausalSky #EconSky #EpiSky #MLSky #StatSky

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Aleksander Molak @alxndrmlk.bsky.social · 27/03/2026

Is the effect heterogeneity in your data real? Testing effect homogeneity and confounding with mixed data In their new working paper, Ana Paula Armendariz and Martin Huber propose a framework for testing the... 1/ #CausalSky #EconSky #EpiSky #StatSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 25/03/2026

You Need an Analysis. Which Statistical Test to Use? Hey there, why not ask Claude? With LLMs, the bottleneck is no longer the speed of access to information or the speed of content generation, but the effort needed to verify the outputs. 1/ #StatSky #EpiSky #BioStats #CausalSky

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Aleksander Molak @alxndrmlk.bsky.social · 23/03/2026

Dropbox Reallocates $25M After a Causal Study In a recent paper, Varun Chivukula et al describe a series of large-scale blackout experiments across marketing channels that inspired the company to reallocate $25M of their marketing budget to more effective channels. 1/ #CausalSky #EconSky #StatSky

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Aleksander Molak @alxndrmlk.bsky.social · 22/03/2026

Dropbox Reallocated $25M of Their Marketing Budget After a Causal Study. In this week's issue of Causal Python Weekly: - Alberto D. Horner reviews a new End-to-End Estimation for Counterfactual Fairness by Yuchen Ma et al. 1/ #CausalSky #EconSky #EpiSky #StatSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 18/03/2026

5 Causal Inference Ideas for the Age of Vibes I'm working on a talk for an event this spring, and I want to ask you for your thoughts. I collected these 5 ideas in response to various queries I got from people across industries and... 1/ #CausalSky #StatSky #EpiSky #EconSky #MLSky

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Aleksander Molak @alxndrmlk.bsky.social · 16/03/2026

What if Two Statistical Tests Disagreed? It's 1894, England. The founder of biometry tosses 12 dice 26,306 times and shares his results with Pearson and Galton. Frank Weldon (formally Walter Frank Raphael Weldon) took this tedious challenge as a part of his broader mission. 1/

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Aleksander Molak @alxndrmlk.bsky.social · 15/03/2026

Is the Effect Heterogeneity in Your Data Real? In today's issue of Causal Python Weekly: - A new paper by Ana Paula Armendariz and Martin Huber on testing for heterogeneity and confounding in high-dimensional mixed data (observational + experimental) 1/ #CausalSky #StatSky #EpiSky #EconSky

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