Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods

By Jeffrey A. Smith, Petra E. Todd. 2001

389 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d8243e7623b2e9e39e531db66da2d21a196aa47b

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Citations in the corpus

Do the Poor Benefit from Devolution Policies? Evidence from Malawi’s Forest Co-Management Program (2006, background): “(For details, see Heckman, Ichimura, and Todd 1997; Smith and Todd 2001, 2005; Dehejia and Wahba 2002).”
Impact of Farmer Field Schools on Agricultural Productivity and Poverty in East Africa (2012, background): “matches the participants and nonparticipants based on the observable characteristics that affect participation in the program and the outcome being measured (Rosenbaum and Rubin 1983; Smith and Todd 2001, 2005).”
Can information alone change behavior? Response to arsenic contamination of groundwater in Bangladesh (2007, neutral): “30 Dehejia (2002), Heckman (1997) and (1998), Smith and Todd (2001)”
Subjective Organizational Performance and Measurement Error: Common Source Bias and Spurious Relationships (2013, methodology): “We treated the problem as the equivalent of selection bias and opted to create a set of statistically similar districts via a propensity score matching technique (see Caliendo and Kopeinig 2008; Dehejia 2005; Dehejia and Wahba 2002; Rosenbaum and Rubin 1985; Smith and Todd 2001).”
Evaluating Anti-Poverty Programs (2005, neutral): “Also see Smith and Todd (2001).”
Yield Effects of Tissue Culture Bananas in Kenya: Accounting for Selection Bias and the Role of Complementary Inputs (2012, methodology): “Statistical methods to deal with selection bias in cross-section data include propensity score matching (PSM) and instrumental variable (IV) approaches (Rosenbaum and Rubin, 1983; Smith and Todd, 2001; Deaton, 2010).”
Drawing conclusions about causes from systematic reviews of risk factors: The Cambridge Quality Checklists (2009, background): “The evidence is mixed on whether propensity scores can reduce bias more than standard matching or regression techniques can (Bloom et al. 2002; Deeks et al. 2003; Dehejia and Wahba 1999; Glazerman et al. 2003; Shah et al. 2005; Smith and Todd 2001 ).”
I Think (I Am Doing Well), Therefore I Am: Assessing the Validity of Administrators' Self-Assessments of Performance (2013, methodology): “We treated the problem as the equivalent of selection bias and opted to create a set of statistically similar districts via a propensity score matching technique (Caliendo and Kopeinig 2008; Dehejia 2005; Dehejia and Wahba 2002; Rosenbaum and Rubin 1985; Smith and Todd 2001).”
Impact of Increased Academic Intensity on Transfer Rates: An Application of Matching Estimators to Student-Unit Record Data (2009, methodology): “The probability p, is defined as (Smith and Todd 2001):”