Causality

CARED: a novel causal action recommendation model improves policy learning by collaborating with human experts and mitigating hidden confounding in observational data.

A new, nuisance-free Distributionally Robust Metric (DRM) is proposed for selecting robust Conditional Average Treatment Effect (CATE) estimators, improving the reliability of personalized decision-ma…

Unified Covariate Adjustment (UCA) offers a scalable, doubly robust estimator for a wide array of causal estimands beyond standard methods.

This paper presents a novel framework for estimating bounds on policy effects under unobserved confounding, offering tighter bounds and robust estimators for higher-dimensional data.

Adaptive experiment design optimizes indirect experiments in complex systems by sequentially narrowing the gap between upper and lower bounds on a targeted query, providing more efficient and informat…

Tangent Space Causal Inference (TSCI) enhances causal discovery in dynamical systems by leveraging vector fields, outperforming existing methods in accuracy and interpretability.

Predicting outcomes of combined sequential interventions is challenging, especially in sparse data. This paper introduces CSI-VAE, a novel compositional model that provides reliable predictions for u…

New streaming algorithms for instrumental variable regression achieve fast convergence rates, solving the problem efficiently without matrix inversions or mini-batches, enabling real-time causal analy…

AI for science faces hidden biases in causal inference; this paper reveals these flaws using ant behavior data, introducing ISTAnt benchmark, and provides guidelines for more accurate causal AI.

Efficiently learn causal graphs from limited interventions using a novel Bayesian algorithm that outperforms existing methods and requires fewer experiments.

QWO: a novel method dramatically speeds up permutation-based causal discovery in linear Gaussian models, enabling the analysis of larger datasets and advancing causal inference.

New orthogonal learner quantifies treatment effect’s randomness, providing sharper insights beyond average effects.

Researchers introduce QIM-compatibility, a novel framework for modeling qualitative relationships in probability distributions using directed hypergraphs, significantly expanding beyond standard condi…

Unmeasured confounders hinder causal inference; this paper introduces a novel method using two pre-treatment text instances and zero-shot models to infer proxies for unobserved confounders, enabling p…

Learning optimal interventions in causal bandits with unknown causal graphs is now efficient; this paper identifies the minimal causal knowledge needed and offers a two-stage algorithm with sublinear …

Causal discovery algorithm CaPS efficiently handles mixed linear and nonlinear relationships in observational data, outperforming existing methods on synthetic and real-world datasets.

Researchers achieve full parameter identifiability in partially observed linear causal models using novel graphical conditions and a likelihood-based estimation method, addressing previous limitations…

Researchers developed a novel, efficient causal discovery method using Probability Generating Functions to identify causal structures within Poisson Branching Structural Causal Models, overcoming limi…

New polynomial-space algorithm for causal parameter identification in linear models vastly improves upon existing methods, showing that this crucial task is computationally hard.

DGES, a novel framework, efficiently detects & handles deterministic relations in causal discovery, enhancing accuracy and scalability for real-world applications.