Hugging Face Trending Papers

Robust Counterfactuals for Markov Decision Processes under Statistical and Causal Uncertainties

arXiv AI

Aug 5

Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models

arXiv:2608. 02893v1 Announce Type: cross Abstract: Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from latent variables.

By Jessica Lally, Milad Kazemi, Nicola Paoletti, David Watson, Sander Beckers

reinforcement-learning

arXiv AI

Oct 15

Causal Identification from Counterfactual Data: Completeness and Bounding Results

arXiv:2602.23541v3 Announce Type: replace Abstract: Previous work establishing completeness results for counterfactual identification has been circumscribed to the setting where the input data belong...

By Arvind Raghavan, Elias Bareinboim

arXiv AI

Jul 8

Safe Bayesian Optimization with Counterfactual Policies

arXiv:2607. 05620v1 Announce Type: cross Abstract: In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below some established threshold.

By Katherine Avery, Bruno Castro da Silva, David Jensen

safety

arXiv Machine Learning

Oct 7

Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models

arXiv:2610.08689v1 Announce Type: new Abstract: Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limit...

By Juliette Sinnott, Amir-Hossein Karimi, Mohammad Kohandel

arXiv AI

Sep 10

AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions

arXiv:2609.07917v1 Announce Type: cross Abstract: Counterfactual explanations formalize "what-if" scenarios by identifying modifications to an input instance that obtain a desired alternative predict...

By Jamie Duell, Alejandro Jimenez Rodriguez, Mahault Albarracin

benchmarks

arXiv AI

Jun 30

Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees

arXiv:2606. 29681v1 Announce Type: new Abstract: Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur.

By Ryohei Oura, Georgios Fainekos, Hideki Okamoto, Bardh Hoxha

reinforcement-learning benchmarks

Hugging Face Trending Papers

Jun 29

Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees

Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur. Probability-raising (PR) causality addresses this by identifying states whose visitation increases the probability of reaching designated states.

reinforcement-learning benchmarks

arXiv AI

Sep 30

Uncertainty-aware Causal Decision Making via Effect Bound Decomposition

arXiv:2601.22736v3 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having...

By Md Musfiqur Rahman, Ziwei Jiang, Hilaf Hasson, Murat Kocaoglu

arXiv AI

Jun 9

UA-DCM: Uncertainty-aware Causal Decision Making via Effect Bound Decomposition

arXiv:2601. 22736v2 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having to perform expensive randomized trials.

By Md Musfiqur Rahman, Ziwei Jiang, Hilaf Hasson, Murat Kocaoglu

arXiv AI

Jul 21

Counterfactual Shapley Credit Assignment

arXiv:2607. 16999v1 Announce Type: cross Abstract: The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents.

By Mingxuan Li, Kaizhan-Lee, Elias Bareinboim

agents reinforcement-learning benchmarks

arXiv AI

Jul 31

Property-driven Causal Abstractions for Markov Decision Processes

arXiv:2607. 26787v1 Announce Type: new Abstract: Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and their valuations.

By Jule Schmidt, Maximilian Weininger, Clemens Dubslaff, David Parker, Nils Jansen

reinforcement-learning benchmarks

Hugging Face Trending Papers

Jun 17

Target-confidence Recourse Using tSeTlin machines: TRUST

Counterfactual explanations are widely used to provide algorithmic recourse in high-stakes decision-making systems. Most existing methods seek the smallest change to an input that flips a model's decision.

benchmarks

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