Robust Causal

Robust causal inference aims to reliably estimate causal effects from observational data, which is often plagued by confounding variables and biases. Current research heavily focuses on developing doubly robust methods, leveraging machine learning techniques like neural networks and targeted learning, to improve the accuracy and robustness of causal effect estimation even with model misspecification. These advancements are crucial for various fields, enabling more reliable evaluations of interventions in areas such as public health, transportation, and social sciences, where randomized controlled trials are often infeasible. The development of robust methods that handle high-dimensional data and address issues like overlap violations is a key area of ongoing investigation.

Papers

November 18, 2024

Robust Causal Analysis of Linear Cyclic Systems With Hidden Confounders
Boris Lorbeer
Native Robustness Causal Pattern Underlying Causal Linear System Robust Causal Causality Method

May 6, 2024

Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao
Causal Effect Estimation Nuisance Parameter Network Interference Robust Causal Learning High Quality

January 1, 2024

Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach
Shuang Li, Ziyuan Pu, Zhiyong Cui, Seunghyeon Lee, Xiucheng Guo, Dong Ngoduy
Causal Effect Structural Causal Model Heterogeneous Treatment Effect Causal Machine Learning Urban Traffic CRASH Bug Robust Causal

February 6, 2023

Causal Estimation of Exposure Shifts with Neural Networks
Mauricio Tec, Kevin Josey, Oladimeji Mudele, Francesca Dominici
Neural Network Causal Inference Causal Effect Causal Effect Estimation Air Pollution Frequency Response Exposure Data Robust Causal

October 15, 2022

Distributionally Robust Causal Inference with Observational Data
Dimitris Bertsimas, Kosuke Imai, Michael Lingzhi Li
Observational Data Unobserved Confounders Marginal Sensitivity Model Robust Causal

September 30, 2022

Causal Estimation for Text Data with (Apparent) Overlap Violations
Lin Gui, Victor Veitch
Causal Effect Causal Effect Estimation Text Data Causal Effect Identification Conditional Mean Robust Causal

November 27, 2021

A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data
Md Saiful Islam, Sahil Shikalgar, Md. Noor-E-Alam
High Dimensional Causal Inference Feature Selection Treatment Effect Robust Evaluation Randomized Controlled Trial Elastic Net Robust Causal