Using the Propensity Score Method to Estimate Causal Effects

364 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Late-career entrepreneurship, income and quality of life (2017, background): “…activity can be restricted to certain times and locations, the autonomy and control to decide when, where, and how to perform work is recognized as the main utility of entrepreneurship compared to full-time organizational employment (Mallett and Wapshott, 2015; Parker, 2009; Rindova et al., 2009).”
The Impact of Entrepreneurship Education in High School on Long-Term Entrepreneurial Performance (2015, methodology): “Third, the nonparametric nature of the matching technique makes it less susceptible to violations of model assumptions (Li, 2013).”
On doing better science: From thrill of discovery to policy implications (2017, background): “Matching of this sort can be achieved in a variety of ways, even on a basic level (Grimes & Schulz, 2005), though ideally should be undertaken with a statistical algorithm (Li, 2013; Niccolai et al., 2007).”
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming (2019, methodology): “There are several approaches for counterfactual inference, including the twin network approach (where approximate inference, e.g. loopy belief propagation, is usually used as in Balke and Pearl (1994)), single-world intervention graphs (Richardson and Robins, 2013), matching (Li, 2013), and more.”