unmeasured-confounding

causarray is a Python module for simultaneous causal inference with an array of outcomes.

Sensitivity Analysis for Difference in adjusted Restricted Mean Survival Time to Unmeasured Confounding

GSAMU: Sensitivity analysis for effects of multiple exposures in the presence of unmeasured confounding: non-Gaussian and time-to-event outcomes

Sensitivity analysis for effects of multiple exposures in the presence of unmeasured confounding

Code for "Evaluating empirical calibration of P-values under unmeasured confounding bias: a simulation study and real-world application" (JCE, 2026)

An Implementation for "LoSAM : Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach"

Implementation of "Off-Policy Interval Estimation with Confounded Markov Decision Process" (JASA, 2022+)

Implementation of "A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes" (ICML)