  Existing weighting methods for treatment effect estimation are often built
upon the idea of propensity scores or covariate balance. They usually impose
strong assumptions on treatment assignment or outcome model to obtain unbiased
estimation, such as linearity or specific functional forms, which easily leads
to the major drawback of model mis-specification. In this paper, we aim to
alleviate these issues by developing a distribution learning-based weighting
method. We first learn the true underlying distribution of covariates
conditioned on treatment assignment, then leverage the ratio of covariates'
density in the treatment group to that of the control group as the weight for
estimating treatment effects. Specifically, we propose to approximate the
distribution of covariates in both treatment and control groups through
invertible transformations via change of variables. To demonstrate the
superiority, robustness, and generalizability of our method, we conduct
extensive experiments using synthetic and real data. From the experiment
results, we find that our method for estimating average treatment effect on
treated (ATT) with observational data outperforms several cutting-edge
weighting-only benchmarking methods, and it maintains its advantage under a
doubly-robust estimation framework that combines weighting with some advanced
outcome modeling methods.
