Causal Discovery in Python. Learning causality from data.
[ICML 2025] R implementation of MIIC_search&score: a search-and-score algorithm for learning ancestral graphs with latent confounders, using multivariate information over ac-connected subset.
Repository for the paper: "Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flow".
This repository contains the code for the paper "Mitigating Shortcut Learning via Feature Disentanglement in Medical Imaging: A Benchmark Study".
Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.
CDAD-UH 1043EQ Data and Society | Fall 2021 | Final Project
Propensity Score based Matching via Distribution Learning
Snapshot of the implementation used to generate figures and numerical results from the paper "Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders", Baitairian et al. (2024).
An Implementation for "LoSAM : Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach"
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