Average Treatment Effects: Double Robustness

Conditional Average Treatment Effects: Overview

Average Treatment Effects: Propensity Scores

Stanford CS230 | Autumn 2025 | Lecture 9: Career Advice in AI

Applied Machine Learning: Secret Sauce

Interview with Jamie Robins on Causal Inference

Philipp Bach and Sven Klaassen: Tutorial on DoubleML for double machine learning in Python and R

Semiparametrics: A Biostatistician’s Toolbox (Prof. Daniel Scharfstein)

Gary King, "Why Propensity Scores Should Not Be Used for Matching"

14. Causal Inference, Part 1

Оценка причинно-следственных связей: взвешивание по обратной вероятности

Doubly-Robust Estimation for Correcting Position-Bias in Clicks for Unbiased Learning to Rank

Double Machine Learning for Causal and Treatment Effects

Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019

What is causal inference, and why should data scientists know? by Ludvig Hult

Conditional Average Treatment Effects: Forests

Dr. Sebastian Teran Hidalgo - Doubly Robust Estimation of Causal Effects in R

An introduction to Causal Inference with Python – making accurate estimates of cause and effect from

Решение неоднородных оценочных уравнений с использованием алгоритмов на основе леса

Susan Athey Guest Talk - Estimating Heterogeneous Treatment Effects

Loss Functions: Treatment Heterogeneity