We introduce priors and algorithms to perform Bayesian inference in Gaussian models defined by acyclic directed mixed graphs. Such a class of graphs, composed of directed and bi-directed edges, is a representation of conditional independencies that is closed under marginalization and arises naturally from causal models which allow for unmeasured confounding. Monte Carlo methods and a variationa...

This paper is concerned with the problem of making causal inferences from observational data, when the underlying causal structure may involve feedback loops. In particular, making causal inferences under the assumption that the causal system which generated the data is linear and that there are no unmeasured common causes (latent variables). Linear causal structures of this type can be represe...

Prior research on Mexican migration has shown that social networks and economic incentives play an important role in determining migration outcomes. We use experimental data from PROGRESA, Mexico's primary poverty-reduction program, to evaluate the effects of conditional cash transfers on migration both domestically and to the United States. Our study complements a growing body of literature ai...

Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in either experimental or observational studies. In this paper, we provide complete graphical and algorithmic conditions for recovering conditional probabilities from sel...

This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfa...

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This paper considers inference of causal structure in a class of graphical models called conditional DAGs. These are directed acyclic graph (DAG) models with two kinds of variables, primary and secondary. The secondary variables are used to aid in the estimation of the structure of causal relationships between the primary variables. We prove that, under certain assumptions, such causal structur...

This paper studies the interrelations between independence or conditional independence and causal relations, defined in terms of functional dependence, that hold among variables of interest within the settable system framework of White and Chalak. We provide formal conditions ensuring the validity of Reichenbach’s principle of common cause and introduce a new conditional counterpart, the condit...

Two experiments compared people’s interpretations of indicative conditionals (e.g., if the shape is blue, then it’s a square), subjunctive conditionals (e.g., if the shape were blue, then it would be a square), causation statements (e.g., the shape being blue causes it to be a square) and prevention statements (e.g., the shape being blue prevents it from being a square). In the first experiment...

OBJECTIVE We tested mediation of birth weight and ADHD symptoms by multiple biologically plausible neurocognitive functions and evaluated familiality of observed indirect effects. METHOD 647 youth from 284 multiplex families with ADHD completed the Arithmetic, Digit Span, Vocabulary, and Block Design subtests of the Wechsler Intelligence Scale for Children (WISC). Multiple mediation tested WI...