Estimation of Local Average Treatment Effect by Data Combination

by   Kazuhiko Shinoda, et al.

It is important to estimate the local average treatment effect (LATE) when compliance with a treatment assignment is incomplete. The previously proposed methods for LATE estimation required all relevant variables to be jointly observed in a single dataset; however, it is sometimes difficult or even impossible to collect such data in many real-world problems for technical or privacy reasons. We consider a novel problem setting in which LATE, as a function of covariates, is nonparametrically identified from the combination of separately observed datasets. For estimation, we show that the direct least squares method, which was originally developed for estimating the average treatment effect under complete compliance, is applicable to our setting. However, model selection and hyperparameter tuning for the direct least squares estimator can be unstable in practice since it is defined as a solution to the minimax problem. We then propose a weighted least squares estimator that enables simpler model selection by avoiding the minimax objective formulation. Unlike the inverse probability weighted (IPW) estimator, the proposed estimator directly uses the pre-estimated weight without inversion, avoiding the problems caused by the IPW methods. We demonstrate the effectiveness of our method through experiments using synthetic and real-world datasets.



page 17

page 18


Individual Treatment Effect Estimation in a Low Compliance Setting

Individual Treatment Effect (ITE) estimation is an extensively researche...

Treatment effect estimation with disentangled latent factors

A pressing concern faced by cancer patients is their prognosis under dif...

Weighting-Based Treatment Effect Estimation via Distribution Learning

Existing weighting methods for treatment effect estimation are often bui...

A nonparametric projection-based estimator for the probability of causation, with application to water sanitation in Kenya

Current estimation methods for the probability of causation (PC) make st...

Treatment Effect Estimation with Efficient Data Aggregation

Data aggregation, also known as meta analysis, is widely used to synthes...

Regression Discontinuity Design with Many Thresholds

Numerous empirical studies employ regression discontinuity designs with ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.