Double Machine Learning for Causal Inference under Shared-State Interference
Researchers and practitioners often wish to measure treatment effects in settings where units interact via markets and recommendation systems. In these settings, units are affected by certain shared states, like prices, algorithmic recommendations or social signals. We formalize this structure, calling it shared-state interference, and argue that our formulation captures many relevant applied settings. Our key modeling assumption is that individuals' potential outcomes are independent conditional on the shared state. We then prove an extension of a double machine learning (DML) theorem providing conditions for achieving efficient inference under shared-state interference. We also instantiate our general theorem in several models of interest where it is possible to efficiently estimate the average direct effect (ADE) or global average treatment effect (GATE).
Code (1)
Tasks
Causal InferenceRecommendation SystemsSimilar Papers 제목 키워드 기반
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. It contains functionalities for valid statistical inference on ca…
BIG-bench Machine LearningvalidDoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The d…
BIG-bench Machine LearningvalidApplied Causal Inference Powered by ML and AI
An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) a…
Causal InferenceCovariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification becomes increasingly difficult. Double/debiased machine learning (DML) facili…
Causal InferenceHyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
Proper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks. While there is an extensive literature on tuning ML learners for prediction, there …
AutoMLCausal Inference