Abstract There are two general views in causal analysis of experimental data: the super population view that the units are an independent sample from some hypothetical infinite population, and the finite population view that the potential outcomes of the experimental units are fixed and the...

Abstract This note illustrates, using simple examples, how causal questions of non-trivial character can be represented, analyzed and solved using linear analysis and path diagrams. By producing closed form solutions, linear analysis allows for swift assessment of how various features of the...

Abstract The structural interpretation of counterfactuals as formulated in Balke and Pearl (1994a,b) [ 1 , 2 ] excludes disjunctive conditionals, such as “had $X$ been $x_1~\mbox{or}~x_2$ ,” as well as disjunctive actions such as $do(X=x_1~\mbox{or}~X=x_2)$ . In contrast, the closest-world...

Abstract Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response. However, many...

Abstract We consider the conditional randomization test as a way to account for covariate imbalance in randomized experiments. The test accounts for covariate imbalance by comparing the observed test statistic to the null distribution of the test statistic conditional on the observed covariate...

Abstract Estimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units. Familiar statistical formalism, experimental designs, and analysis methods assume the absence...

Abstract An instrumental variable can be used to test the causal null hypothesis that an exposure has no causal effect on the outcome, by assessing the association between the instrumental variable and the outcome. Under additional assumptions, an instrumental variable can be used to estimate...

Abstract In randomized controlled trials, the evaluation of an overall treatment effect is often followed by effect modification or subgroup analyses, where the possibility of a different magnitude or direction of effect for varying values of a covariate is explored. While studies of effect...

Abstract We study the framework for semi-parametric estimation and statistical inference for the sample average treatment-specific mean effects in observational settings where data are collected on a single network of possibly dependent units (e.g., in the presence of interference or spillover)....