Causal inference in the empirical sciences is based on counterfactuals. This paper presents the counterfactual account of causation in terms of Lewis's possible-world semantics, and reformulates the statistical potential outcome framework and its underlying assumptions using counterfactual...

Causal inference in the empirical sciences is based on counterfactuals. This paper presents the counterfactual account of causation in terms of Lewis's possible-world semantics, and reformulates the statistical potential outcome framework and its underlying assumptions using counterfactual...

This paper investigates a Dutch data set on vacancy durations and numbers of applicants to inquire employer's search strategies. A non-sequential search process assumes that most vacancies are filled from a pool of applicants, which is formed shortly after the posting of the vacancy. The time...

This paper investigates the method of matching regarding two crucial implementation choices, the distance measure and the type of algorithm. We implement optimal full matching - a fully efficient algorithm - and present a framework for statistical inference. The implementation uses data from the...

This paper investigates the method of matching regarding two crucial implementation choices, the distance measure and the type of algorithm. We implement optimal full matching a fully efficient algorithm and present a framework for statistical inference. The implementation uses data from the...