Average Treatment Effects - Transparencies

Average Treatment Effects - Transparencies - Average...

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Average Treatment E/ects Lecture for Economics 245A Douglas G. Steigerwald UC Santa Barbara September 2011
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Overview goal: estimate impact of treatment restriction: binary treatment example: job training program assume: observations are unconfounded covariates adjust for all di/erences between treatment and control groups individuals may select into treatment, but do so on the basis of observable covariates (selection on observables)
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Implementation Variety of Estimators matching propensity score regression procedure may be matching in combination with regression Key Issues 1 overlap in covariate distributions between treatment and control groups - problem if 2% of control group are HS dropouts and 50% of treatment group are HS dropouts 2 validity of unconfoundedness assumption - assess with lagged outcome values as pseudo-outcomes 3 variance estimation - bootstrap invalid for matching estimators
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Potential Outcome Framework Rubin 1974 pair of potential outcomes for each unit, i = 1 , . . . , n Y i ( 0 ) - outcome under control treatment Y i ( 1 ) - outcome under active treatment for each unit, observe treatment indicator, W i 2 f 0 , 1 g W i = 0 indicates control treatment covariate vector, X i covariates must not be a/ected by treatment
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This note was uploaded on 12/26/2011 for the course ECON 245a taught by Professor Staff during the Fall '08 term at UCSB.

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Average Treatment Effects - Transparencies - Average...

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