Practical propensity score matching: a reply to Smith and Todd

By Rajeev Dehejia. 2005

399 citations recorded by the source corpus.

Subfields: Soil Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bd50a77b47b11e15040cec722513821479152637

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Citations in the corpus

Evaluating Anti-Poverty Programs (2005, neutral): “Returning to the same data set used by the La Londe (1986) study (described in Section 3), Dehejia and Wahba (1999) found that PSM achieved a fairly good approximation – much better than the NX methods studied by La Londe. It appears that the poor performance of the NX methods used by La Londe stemm”
Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R (2008, neutral): “Attached is the full output from MatchBalance using one of Dehejia and Wahba’s propensity score models:”
Do differences in the scale of irrigation projects generate different impacts on poverty and production (2011, methodology): “…the data were not generated as a random experiment, drawing on a growing theoretical and empirical literature (Heckman, Ichimura, and Todd 1997; Dehejia and Wahba 2002; Jalan and Ravallion 2003; Bertrand, Duflo, and Mullainathan 2004; Smith and Todd 2005; Dehejia 2005; Gilligan and Hoddinott 2007).”
Systematic Review. What is the evidence of the impact of microfinance on the well-being of poor people? (2011, background): “However, Smith and Todd (2005) argued that the PSM estimates calculated by Dehejia and Wahba (1999, 2002) are sensitive to their choice of a particular subsample of LaLonde’s (1986) data.”
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).”
Income and employment effects of shale gas extraction windfalls: Evidence from the Marcellus region (2014, methodology): “The PSM procedure has some desirable characteristics over the standard regression model (Dehejia, 2005).”
Matching with Propensity Scores to Reduce Bias in Observational Studies (2006, background): “Their results, however, sparked a long controversy that is well summarized in Smith and Todd (2005) and Dehejia (2005).”
Does Microfinance Reduce Poverty in Bangladesh? New Evidence from Household Panel Data (2012, background): “Models and methodological issues for propensity score matching estimation are discussed in, for example, Becker and Ichino (2002), Dehejia ( 2005 ), Dehejia and Wahba (2002), Heckman et al.”
Comment: The Design and Analysis of Gold Standard Randomized Experiments (2008, background): “A positive outcome of the debate, however, is that in a reply, Dehejia (2005) provided many new analyses (including on the broader data set) and more fully explored when we might expect the method to break down.”
How Important is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration (2006, neutral): “Dehejia and Wahba (2002) and Smith and Todd (2005a,b) use the same NSW data as Lalonde (1986) to examine whether propensity-score matching techniques yield”
Propensity score matching and variations on the balancing test (2011, neutral): “Dehejia and Wahba (1999, 2002) found that using propensity score methods, they could come close to replicating the experimental benchmark results. However, questions have been raised regarding the robustness of this finding. Smith and Todd (2005a) argue that the Dehejia and Wahba (1999, 2002) result”