all AI news
Treatment Effect Estimation from Observational Network Data using Augmented Inverse Probability Weighting and Machine Learning. (arXiv:2206.14591v2 [stat.ME] UPDATED)
stat.ML updates on arXiv.org arxiv.org
Causal inference methods for treatment effect estimation usually assume
independent experimental units. However, this assumption is often questionable
because experimental units may interact. We develop augmented inverse
probability weighting (AIPW) for estimation and inference of causal treatment
effects on dependent observational data. Our framework covers very general
cases of spillover effects induced by units interacting in networks. We use
plugin machine learning to estimate infinite-dimensional nuisance components
leading to a consistent treatment effect estimator that converges at the
parametric rate …
arxiv data machine machine learning network probability treatment