$A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts$

A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts

7 April 2019

Ezequiel Smucler

Papers citing "A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts"

9 / 9 papers shown

Title
Treatment Effect Estimation with Observational Network Data using Machine Learning Corinne Emmenegger Meta-Lina Spohn Timon Elmer Peter Buhlmann CML 60 3 1 20 Jan 2025
Reconciling model-X and doubly robust approaches to conditional independence testing Ziang Niu Abhinav Chakraborty O. Dukes Eugene Katsevich 23 7 0 27 Nov 2022
Assumption-lean inference for generalised linear model parameters S. Vansteelandt O. Dukes CML 21 49 0 15 Jun 2020
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond Nathan Kallus Xiaojie Mao Masatoshi Uehara 25 25 0 30 Dec 2019
Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models A. Rotnitzky Ezequiel Smucler CML 11 30 0 01 Dec 2019
Double-estimation-friendly inference for high-dimensional misspecified models Rajen Dinesh Shah Peter Buhlmann 18 10 0 24 Sep 2019
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning Nathan Kallus Masatoshi Uehara OffRL 14 87 0 12 Sep 2019
Characterization of parameters with a mixed bias property A. Rotnitzky Ezequiel Smucler J. M. Robins 14 66 0 07 Apr 2019
Automatic Debiased Machine Learning of Causal and Structural Effects Victor Chernozhukov Whitney Newey Rahul Singh CML AI4CE 24 103 0 14 Sep 2018