The key question in program evaluation is whether the intervention is working or not. But to answer that question, first program outcomes must be attributed to the intervention, and not some other factor. The attribution issue arises in this context because a program, as a means-ends relationship, interacts with contextual factors that may strengthen, reduce, or break the intended causal link b...

Galles and Pearl [1998] claimed that “for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis’s [possible-worlds] framework.” This claim is examined carefully, with the goal of clarifying the exact relationship between causal models and Lewis’s framework. Recursive models are shown to correspond precisely to a subclass of ...

Individuals often have heterogeneous outcomes after interventions. As a result, clinicians constantly ask themselves, given a patient’s history, what would happen to the patient’s clinical trajectory if they were given one treatment versus another. In order to target care, estimating how outcomes or responses to treatments will vary across individuals is critical. However, in practice it is oft...

Concepts concerning mediation in the causal inference literature are reviewed. Notions of direct and indirect effects from a counterfactual approach to mediation are compared with those arising from the standard regression approach to mediation of Baron and Kenny (1986), commonly utilized in the social science literature. It is shown that concepts of direct and indirect effect from causal infer...

This paper presents a general theory of causation based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory subsumes and unifies current approaches to causation, including graphical, potential outcome, probabilistic, decision analytical, and structural equation models, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and cou...

Augmenting the graphoid axioms with three additional rules enables us to handle independencies among observed as well as counterfactual variables. The augmented set of axioms facilitates the derivation of testable implications and ignorability conditions whenever modeling assumptions are articulated in the language of counterfactuals. 1 Motivation Consider the causal Markov chain X → Y → Z whic...

We develop an approach to conceptualizing causal effects in longitudinal settings with time-varying treatments and time-varying outcomes. The classic potential outcome approach to causal inference generally involves two time periods: units of analysis are exposed to one of two possible values of the causal variable, treatment or control, at a given point in time, and values for an outcome are a...

Acknowledgements I would like to give special thanks to my advisor, Professor Kevin Hoover who took my shortcomings and successes with equal stride, and who struggled through the process alongside me. But how will you look for something when you don't in the least know what it is? How on earth are you going to set up something you don't know as the object of your search? To put it another way, ...

We extend Robins’ theory of causal inference for complex longitudinal data to the case of continuously varying as opposed to discrete covariates and treatments. In particular we establish versions of the key results of the discrete theory: the g-computation formula and a collection of powerful characterizations of the g-null hypothesis of no treatment effect. This is accomplished under natural ...