Of widespread interest in social science are observational studies in which entities (persons, schools, states, countries, etc.) are exposed to varied treatment conditions over time. As in all observational studies, the non-randomized assignment of treatments poses challenges to valid causal inference. An attractive feature of panel studies with time-varying treatments, however, is that the des...

This paper unifies three complementary approaches to defining, identifying, and estimating causal effects: the classical structural equations approach of the Cowles Commision; the treatment effects framework of Rubin (1974) and Rosenbaum and Rubin (1983); and the Directed Acyclic Graph (DAG) approach of Pearl. The settable system framework nests these prior approaches, while affording significa...

Using microdata from CoreLogic and Gallup between 2008 and 2014, we study the impact of foreclosures on individual well-being. We identify the causal effect of foreclosures by instrumenting them using variation in interest rate spikes associated with different types of adjustable rate mortgages (ARMs), conditional on controls and zipcode and time fixed effects. We find that a 10% rise in forecl...

Spending on big-time college athletics is often justified on the grounds that athletic success attracts students and raises donations. We exploit data on bookmaker spreads to estimate the probability of winning each game for college football teams. We then condition on these probabilities using a propensity score design to estimate the effects of winning on donations, applications, and enrollme...

A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller f...

In this working paper, Jackson presents a model where students have cognitive and noncognitive ability and a teacher’s effect on long-run outcomes is a combination of his or her effect on both ability types. Conditional on cognitive scores, an underlying noncognitive factor associated with student absences, suspensions, grades, and grade progression is strongly correlated with long-run educatio...

structures, 3accidentally correlated, 6adding-arrows, 36ancestrally, 31atomic states, 37 background knowledge, 26Bayesian network, 2Bayesian Networks Maximise En-tropy, 30 causal extension, 6causal irrelevance, 28causal Markov condition, 1, 3causal restriction, 8, 9conditional mutual information, 40constrained network, 40correlation restrictio...

Drawing inferences about the effects of treatments and actions is a common challenge in economics, epidemiology, and other fields. We adopt Rubin’s potential outcomes framework for causal inference and propose two methods serving complementary purposes. One can be used to estimate average causal effects, assuming no confounding given measured covariates. The other can be used to assess how the ...

Using microdata from CoreLogic and Gallup between 2008 and 2014, we study the impact of foreclosures on individual well-being. We identify the causal effect of foreclosures by instrumenting them using variation in interest rate spikes associated with different types of adjustable rate mortgages (ARMs), conditional on controls and zipcode and time fixed effects. We find that a 10% rise in forecl...

As a causality criterion we propose the conditional relative entropy. The relationship with information theoretic functionals mutual information and entropy is established. The conditional relative entropy criterion is compared with 3 well-established techniques for causality detection: ‘Sims’, ‘GewekeMeese-Dent’ and ‘Granger’. It is shown that the conditional relative entropy, as opposed to th...