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Propensity Scores and Causal Inference Using Machine Learning Methods

Austin Nichols and Linden McBride

2017 Stata Conference from Stata Users Group

Abstract: We compare a variety of methods for predicting the probability of a binary treatment (the propensity score), with the goal of comparing otherwise like cases in treatment and control conditions for causal inference about treatment effects. Better prediction methods can under some circumstances improve causal inference both by reducing the finite sample bias and variability of estimators, but sometimes better predictions of the probability of treatment can increase bias and variance, and we clarify the conditions under which different methods produce better or worse inference (in terms of mean squared error of causal impact estimates).

New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
Date: 2017-08-10
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http://fmwww.bc.edu/repec/scon2017/Baltimore17_Nichols.pdf

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Persistent link: https://EconPapers.repec.org/RePEc:boc:scon17:13

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