Semiparametric Estimation on Multi-treatment Causal Effects via Cross-Fitting

by   Jingying Zeng, et al.

Causal inference is a critical research area with multi-disciplinary origins and applications, ranging from statistics, computer science, economics, psychology to public health. In many scientific research, randomized experiments provide a golden standard for estimation of causal effects for decades. However, in many situations, randomized experiments are not feasible in practice so that practitioners need to rely on empirical investigation for causal reasoning. Causal inference via observational data is a challenging task since the knowledge of the treatment assignment mechanism is missing, which typically requires non-testable assumptions to make the inference possible. For several years, great effort has been devoted to the research of causal inference for binary treatments. In practice, it is also common to use observational data on multiple treatment comparisons. Within the potential outcomes framework, we propose a generalized cross-fitting estimator (GCF), which generalizes the doubly robust estimator with cross-fitting for binary treatment to multiple treatment comparisons and provides rigorous proofs on its statistical properties. This estimator permits the use of more flexible machine learning methods to model the nuisance parts, and based on relatively weak assumptions, while there is still a theoretical guarantee for valid statistical inference. We show the asymptotic properties of the GCF estimators, and provide the asymptotic simultaneous confidence intervals that achieve the semiparametric efficiency bound for average treatment effect. The performance of the estimator is accessed through simulation study based on the common evaluation metrics generally considered in the causal inference literature.


page 1

page 2

page 3

page 4


Doubly robust treatment effect estimation with missing attributes

Missing attributes are ubiquitous in causal inference, as they are in mo...

Doubly robust confidence sequences for sequential causal inference

This paper derives time-uniform confidence sequences (CS) for causal eff...

Targeting Learning: Robust Statistics for Reproducible Research

Targeted Learning is a subfield of statistics that unifies advances in c...

Causal Inference with Noisy and Missing Covariates via Matrix Factorization

Valid causal inference in observational studies often requires controlli...

Debiased Inference on Identified Linear Functionals of Underidentified Nuisances via Penalized Minimax Estimation

We study generic inference on identified linear functionals of nonunique...

Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms

This paper provides a link between causal inference and machine learning...

Efficient Estimation for Staggered Rollout Designs

Researchers are frequently interested in the causal effect of a treatmen...

Please sign up or login with your details

Forgot password? Click here to reset