Across the social sciences, lagged explanatory variables are a common strategy to confront challenges to causal identification using observational data. We show that “lag identification” — the use of lagged explanatory variables to solve endogeneity problems — is an illusion: lagging...

This chapter uses the marginal treatment effect (MTE) to unify and organize the econometric literature on the evaluation of social programs. The marginal treatment effect is a choice-theoretic parameter that can be interpreted as a willingness to pay parameter for persons at a margin of...

This paper describes a modelling methodology for multivariate stochastic processes. The concept of multiple causality is discussed and a procedure to detect multiple causality is suggested. The data of a major Canadian supermarket is analyzed and a multivariate autoregressive model for this...

In this paper, I try to tame "Basu's elephants" (data with extreme selection on observables). I propose new practical large-sample and finite-sample methods for estimating and inferring heterogeneous causal effects (under unconfoundedness) in the empirically relevant context of limited overlap....

A new panel data test for Granger causality is presented that can be applied to panels with more time series observations than cross-sections. Inconsistency in these models with lagged dependent variables and fixed effects is avoided by differencing and pooling the data. The joint significance...