We address the problem of causal discovery in the two-variable case given a sample from their joint distribution. The proposed method is based on a known assumption that, if X → Y (X causes Y ), the marginal distribution of the cause, P (X), contains no information about the conditional distribution P (Y |X). Consequently, estimating P (Y |X) from P (X) should not be possible. However, estimati...

Estimation of causal effects of time-varying exposures using longitudinal data is a common problem in epidemiology. When there are time-varying confounders, which may include past outcomes, affected by prior exposure, standard regression methods can lead to bias. Methods such as inverse probability weighted estimation of marginal structural models have been developed to address this problem. Ho...

Given a randomized treatment Z, a clinical outcome Y, and a biomarker S measured some fixed time after Z is administered, we may be interested in addressing the surrogate endpoint problem by evaluating whether S can be used to reliably predict the effect of Z on Y. Several recent proposals for the statistical evaluation of surrogate value have been based on the framework of principal stratifica...

OBJECTIVE To estimate the population-level causal effect of source of payment for HIV medication on treatment adherence using Marginal Structural Models. METHODS Data were obtained from an observational cohort of 76 HIV-infected individuals with at least 24 weeks of antiretroviral therapy treatment from 2002 to 2007 in Kampala, Uganda. Adherence was the primary outcome and it was measured usi...

This paper considers the problem of inferring a discrete joint distribution from a sample subject to selection. Abstractly, we want to identify a distribution p(x,w) from its conditional p(x |w). We introduce new assumptions on the marginal model for p(x), under which generic identification is possible. These assumptions are quite general and can easily be tested; they do not require precise ba...

BACKGROUND In the absence of unmeasured confounding factors and model misspecification, standard methods for estimating the causal effect of time-varying treatments on survival are biased when (i) there exists a time-dependent risk factor for survival that also predicts subsequent treatment and (ii) past treatment history predicts subsequent risk factor level. In contrast, structural models pro...

The variability of structure in a finite Markov equivalence class of causally sufficient mod­ els represented by directed acyclic graphs has been fully characterized. Without causal suf­ ficiency, an infinite semi-Markov equivalence class of models has only been characterized by the fact that each model in the equiva­ lence class entails the same marginal statis­ tical dependencies. In this pap...

We consider graphs that represent pairwise marginal independencies amongst a set of variables (for instance, the zero entries of a covariance matrix for normal data). We characterize the directed acyclic graphs (DAGs) that faithfully explain a given set of independencies, and derive algorithms to efficiently enumerate such structures. Our results map out the space of faithful causal models for ...

In this commentary, structural equation models (SEMs) are discussed as a tool for epidemiologic analysis. Such models are related to and compared with other analytic approaches often used in epidemiology, including regression analysis, causal diagrams, causal mediation analysis, and marginal structural models. Several of these other approaches in fact developed out of the SEM literature. Howeve...

The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal risks are equal in the two groups. This null hypothesis is referred to as the weak causal null hypothesis. Nevertheless, at present, hypothesis tests applied in actual randomized t...