Causality

Exogenous Matching learns optimal proposals for efficient counterfactual estimation by transforming variance minimization into conditional distribution learning, outperforming existing methods.

This study develops a novel two-stage framework for accurately predicting conditional average treatment effects using both observational data and weak instrumental variables, overcoming limitations of…

This research introduces LFHSIC, a novel, linear-time independence test that significantly outperforms existing methods, especially for high-dimensional data, by learning optimal Fourier feature pairs…

This paper presents novel graphical criteria and estimators for accurately evaluating policy effectiveness across multiple experimental datasets, even when data distributions differ.

Causal inference revolutionized: New framework estimates causal effects from exchangeable data, enabling simultaneous causal discovery and effect estimation via the Do-Finetti algorithm.

This paper introduces graphical criteria and an algorithm for disentangling causal factors from heterogeneous data in non-Markovian settings, advancing causal representation learning.

This research introduces a novel plug-and-play module that efficiently integrates prior partial order constraints into differentiable structure learning, significantly improving structure recovery qua…

This paper proposes novel measures to detect and quantify confounding biases from observational data using causal mechanism shifts, even with unobserved confounders.

A new Covariate Shift Corrected Pearson Chi-squared Conditional Randomization (csPCR) test accurately assesses conditional independence even when data distributions vary between source and target popu…

AI decision support systems can unintentionally harm users; this paper introduces a novel framework to design systems that minimize this counterfactual harm, balancing accuracy and user well-being.

Neural causal models consistently estimate partial causal effects, even with continuous/categorical variables, thanks to Lipschitz regularization.

Researchers introduce the Conditional Quantile Comparator (CQC) for analyzing heterogeneous treatment effects, offering an improved approach by combining the strengths of CATE and CQTE while overcomin…

ID-GEN: Sample high-dimensional interventional distributions using any conditional generative model!

Researchers strengthen understanding of transformer self-attention by proving asymptotic convergence to single clusters under causal masking, linking it to the Rényi parking problem.

ChronoEpilogi efficiently finds all minimal sets of time-series variables optimally predicting a target, improving forecasting while providing crucial insights for knowledge discovery and causal model…

Causal assumptions drastically alter predictor merging, with CMAXENT revealing logistic regression for causal and LDA for anticausal directions.

Researchers introduce a non-parametric causal inference framework to analyze closed-loop optogenetics designs, revealing previously hidden causal effects of neural circuit manipulations on behavior.

This paper introduces a novel algorithm to accurately compute causal effects within specific sub-populations, even when hidden factors influence the data, advancing causal inference significantly.

CASCADE algorithm unveils hidden causal structures in event sequences by minimizing description length, surpassing existing Granger causality-based methods.

MC-EIF automates efficient statistical estimation for high-dimensional models, integrating seamlessly with existing differentiable probabilistic programming systems and achieving optimal convergence r…