Online Estimation of the Average Treatment Effect

Authored by: Sam Lendle

Handbook of Big Data

Print publication date:  February  2016
Online publication date:  February  2016

Print ISBN: 9781482249071
eBook ISBN: 9781482249088
Adobe ISBN:

10.1201/b19567-32

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Abstract

Drawing causal inferences from observational data requires making strong assumptions about the causal process from which the data are generated, followed by a statistical analysis of the observational dataset. Though we must make causal assumptions, we often know little about the data-generating distribution. This means we generally cannot make strong statistical assumptions so we estimate a statistical parameter in a nonparametric or semi-parametric statistical model. Semiparametric efficient estimators, that is, estimators that achieve the minimum asymptotic variance bound, such as augmented inverse probability of treatment weighted (A-IPTW) estimators [11] and targeted minimum loss-based estimators (TMLE) [15,18], have been developed for a variety of statistical parameters with applications in causal inference.

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