Simulation-Based Sensitivity Analysis for Matching Estimators:
TL;DR: In this paper, a Stata program (sensatt) is presented that implements the sensitivity analysis for matching estimators proposed by Ichino, Mealli, and Nannicini.
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Abstract: This article presents a Stata program (sensatt) that implements the sensitivity analysis for matching estimators proposed by Ichino, Mealli, and Nannicini (Journal of Applied Econometrics, forthcoming). The analysis simulates a potential confounder to assess the robustness of the estimated treatment eects with respect to deviations from the conditional independence assumption. The program uses the commands for propensity-score matching (att* ) developed by Becker and Ichino (Stata Journal 2: 358{377). I give an example by using the National Supported Work demonstration, widely known in the program evaluation literature.
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Citations
Sensitivity Analysis for Average Treatment Effects
TL;DR: In this article, the bounding approach proposed by Rosenbaum (Observational Studies, 2nd ed., New York: Springer), where mhbounds lets the researcher determine how strongly an unmeasured variable must influence the selection process to undermine the implications of the matching analysis.
From temporary help jobs to permanent employment: What can we learn from matching estimators and their sensitivity?
TL;DR: In this article, a simulation-based sensitivity analysis was proposed to evaluate the effect of temporary work agency (TWA) assignments on the probability of finding a stable job in the USA and Europe.
Life satisfaction and self-employment: a matching approach
TL;DR: This article found that individuals who move from regular employment into self-employment experience an increase in life satisfaction (up to 2 years later), while individuals moving from unemployment to self-employee are not more satisfied than their counterparts moving from employment to regular employment.
Causal inference with observational data
TL;DR: In this article, the problems with inferring causal relationships from nonexperimental data are briefly reviewed, and four broad classes of methods designed to allow estimation of and inference about causal parameters are presented.
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What is the evidence of the impact of microfinance on the well-being of poor people?
Maren Duvendack,Richard Palmer-Jones,James Copestake,Lee Hooper,Yoon K. Loke,Nitya Rao +5 more
- 01 Aug 2011
TL;DR: In this article, the authors revisited the evidence of micro-finance evaluations focusing on the technical challenges of conducting rigorous microfinance impact evaluations, and concluded that no well-known study robustly shows any strong impacts of micro finance, while anecdotes and other inspiring stories purported to show that micro finance can make a real difference in the lives of those served.
References
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TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Estimating causal effects of treatments in randomized and nonrandomized studies.
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
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Some practical guidance for the implementation of propensity score matching
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TL;DR: Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects as discussed by the authors, but empirical examples can be found in very diverse fields of study, and each implementation step involves a lot of decisions and different approaches can be thought of.
Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme
TL;DR: This paper decompose the conventional measure of evaluation bias into several components and find that bias due to selection on unobservables, commonly called selection bias in econometrics, is empirically less important than other components, although it is still a sizeable fraction of the estimated programme impact.
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Propensity score-matching methods for nonexperimental causal studies
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TL;DR: In this article, the authors consider causal inference and sample selection bias in nonexperimental settings in which few units in the nonex-experiment comparison group are comparable to the treatment units, and selecting a subset of comparison units similar to treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics.