Causality

This paper presents novel theoretical results enabling the analytical calculation of Partial Information Decomposition for various probability distributions, including those relevant to neuroscience, …

New research quantifies how search engines steer web traffic by subtly changing results, offering a powerful method for antitrust investigations and digital market analysis.

This paper introduces a unified ITR estimation framework using covariate balancing weights, achieving significant gains in robustness and effectiveness compared to existing methods.

Adaptive experimentation tackles confounding in online A/B tests using encouragement designs and a novel linear bandit approach, achieving near-optimal sample complexity.

S²GCSL: A novel scalable Granger causal structural learning approach efficiently identifies root causes of telecommunication network alarms by leveraging a linear kernel and incorporating expert knowl…

Estimating heterogeneous treatment effects (CATE) under unmeasured confounding is revolutionized by a novel non-parametric direct learning approach using instrumental variables, offering efficient and…

This paper introduces a compositional framework for algebraic circuits, deriving novel tractability conditions for compositional inference queries and unifying existing results.