Propensity Score-Matching Methods for Nonexperimental Causal Studies
Columbia University · Morgan Stanley (United States)
Abstract
This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to…
Citation impact
- FWCI
- 63.91
- Percentile
- 100%
- References
- 48
Authors
2Topics & keywords
- Propensity score matching
- Causal inference
- Matching (statistics)
- Selection bias
- Selection (genetic algorithm)
- Benchmark (surveying)
- Econometrics
- Sample (material)