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Tim Woydt

@philosotim.bsky.social

124 followers 343 following 3 posts

AI Researcher & Founder | Interdisciplinary Mathematician | Knowledge Engineering, Causality, Logic, Ethics | PhD Candidate @ AIML Lab, TU Darmstadt

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Tim Woydt @philosotim.bsky.social · 26/09/2026

Huge thanks to my amazing coauthors @pauldavidzuercher.bsky.social, @zm388.bsky.social, @moritzwillig.bsky.social and @kerstingaiml.bsky.social! Stay tuned for more updates!

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Tim Woydt @philosotim.bsky.social · 26/09/2026

In Certified Policy Optimisation for Nested Causal Bandits via PAC-Bayes Risk, we introduce a methodology for optimising hierarchical decision-making with risk-certified policy handover.

Abstract:
Critical sequential decisions are rarely single-timescale: a strategic decision causally shapes the context in which every subsequent tactical choice is made; standard bandit and reinforcement-learning theory does not capture this causal coupling between timescales. We formalise the problem class as Nested Contextual Causal Bandits (NCCBs), a hierarchical SCM where each level's action sets the next level's context distribution, and propose Nested Causal Thompson Sampling (NCTS), which draws one mechanism-factorised belief per episode and acts recursively under it. Our main theoretical result is a causal PAC-Bayesian excess-risk bound that certifies any candidate deployment policy from historic data alone, off-policy and anytime, answering the deployment question: can we trust this agent here, and at what risk? Experiments on a hierarchical SCM show that, against a matched RFF-GP joint regression on the same function class, the factorised SCM-mechanism posterior transfers significantly better zero-shot under exogenous distribution shifts, the recursive meta-to-inner commit significantly dominates the joint-commit alternative in distribution, and the certificate significantly contracts as offline data accumulates. Combining these results, we establish progressive certified handover, a safe-deployment method: each timescale flips from a legacy controller to NCTS when gains can be certified, independently of the others.

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Tim Woydt @philosotim.bsky.social · 26/09/2026

Very happy to share two new papers accepted at #NeurIPS2026! 🎉   In Tracing Actual Causes with Counterfactual Witness Maps, we present an algorithm for complete enumeration of actual causes in structural causal models.

TL;DR: We introduce witness maps a formal method allowing for enumeration of all actual causes in acyclic SCM.

Abstract:
Halpernian actual causation, the formal study of which events in a specific situation caused a specific outcome in acyclic structural causal models, underpins legal responsibility, moral blame, and the assessment of causal harm. Despite the rich logical framework, no prior sound and complete enumeration procedure is known for finding all actual causes in a given setting, blocking downstream tasks such as responsibility attribution, blame assessment, and causal-harm grading that aggregate over the full cause set. We close this gap by introducing witness targets: sets of joint configurations containing every counterfactual witness. In combination with an ancestor intervention grammar we derive a finite directed acyclic witness map. We prove that for every actual cause there exists a contingency set such that the joint intervention set appears as a node of this map, and present an algorithm for Tracing Actual Causes (TrAC) that is sound and complete for enumerating all actual causes of any target event. We compare TrAC empirically with existing single-cause baselines on random Boolean structural causal models, demonstrating practical feasibility for graphs of up to 15 nodes.

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Moritz Willig @moritzwillig.bsky.social · 04/11/2025

We show applications to long-term outcomes and decision making, developing our works on 'causal parrots' openreview.net/pdf?id=tv46t... and meta-causality openreview.net/pdf?id=J9Vog... . Thanks to my coauthors @philosotim.bsky.social, @devendradhami.bsky.social and @kerstingaiml.bsky.social! 2/4

openreview.net

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Moritz Willig @moritzwillig.bsky.social · 04/11/2025

I'm excited to share some updates: 1) Our paper "When Causal Dynamics Matter: Adapting Causal Strategies through Meta-Aware Interventions" (openreview.net/pdf?id=3fpYX...) will be at #NeurIPS2025 We introduce Meta-Causal Analysis to model qualitative transitions of causal systems. 1/4

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Antonia Wüst @toniwuest.bsky.social · 02/05/2025

We also identified 10 particularly challenging Bongard Problems that none of the models could solve under any setting. The challenge remains wide open! 3 examples of the challenging BPs:

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Antonia Wüst @toniwuest.bsky.social · 02/05/2025

Interestingly, success in solving the BPs (Open Question) doesn't translate to correctly categorizing individual images 👉 the sets of BPs solved in each task are not the same! This suggests that getting the right final answer doesn’t always mean genuine understanding 🤔

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Antonia Wüst @toniwuest.bsky.social · 02/05/2025

Our evaluation shows the top-performing model (o1) solved 43 out of 100 problems, with the others trailing far behind. There’s still a long way to go for current AI models!

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Antonia Wüst @toniwuest.bsky.social · 02/05/2025

Excited to share that our paper got accepted at #ICML2025!! 🎉 We challenge Vision-Language Models like OpenAI’s o1 with Bongard problems, classic visual reasoning challenges and uncover surprising shortcomings. Check out the paper: arxiv.org/abs/2410.19546 & read more below 👇

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Moritz Willig @moritzwillig.bsky.social · 17/04/2025

I'm excited to present our spotlight on meta-causal models at #ICLR2025 next week. We model evolving causal graphs in dynamic systems. Applications to inference and attribution of agent actions. Paper: openreview.net/forum?id=J9V... Visit our poster #441 during the Sat 3pm session.

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Kristian Kersting @kerstingaiml.bsky.social · 24/01/2025

Thrilled to share our #ICLR2025 work on Meta-Causal States! 🌟 Causal graphs evolve with dynamic systems & agent actions. We show how to cluster causal models by qualitative behavior, revealing hidden dynamics & emergent relationships 🚀 #Causality #ML arxiv.org/abs/2410.13054

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Devendra Singh Dhami @devendradhami.bsky.social · 01/12/2024

This might be interesting for the same. We provide the concept of meta causal states that can be used to analyze the changes in the causal graph. Work with @moritzwillig.bsky.social @florianbusch.bsky.social @kerstingaiml.bsky.social et al. arxiv.org/abs/2410.13054

arxiv.org

Systems with Switching Causal Relations: A Meta-Causal Perspective

Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping points in the environme...

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