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Model-free causal inference of binary experimental data

Authors :
Ding, Peng
Miratrix, Luke W.
Publication Year :
2017

Abstract

For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. We also introduce methods for likelihood and Bayesian inference based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have some properties superior to moment-based ones such as only giving estimates in regions of feasible support. Due to the lack of identification of the causal model, we also propose a sensitivity analysis approach which allows for the characterization of the impact of the association between the potential outcomes on statistical inference.

Subjects

Subjects :
Statistics - Methodology

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1705.08526
Document Type :
Working Paper