Experimental studies in which treatment assignment is random but subject compliance is imperfect may be susceptible to bias; the actual effect of the treatment may deviate appreciably from the mean difference between treated and untreated subjects. This paper establishes universal formulas that can be used to bound the actual treatment effect in any experiment for which compliance data is available and in which the assignment influences the response only through the treatment given. Using a linear programming analysis, we present formulas that provide the tightest bounds that can be inferred on the average treatment effect, given an empirical distribution of assignments, treatments, and responses. The application of these results is demonstrated on data that relates cholesterol levels to cholestyramine treatment ([Lipid Research Clinic Program 84]).

This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.

Abstract: This review presents empirical researchers with recent advances in causal inference, and stresses 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 underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects ” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret, ” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiosis analysis that uses the strong features of both.

This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses 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 underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis.

<F4.554e+05> Epidemiologic methods were developed to prove general causation: identifying exposures that increase the risk of particular diseases. Courts often are more interested in specific causation: on balance of probabilities, was the plainti#'s disease caused by exposure to the agent in question? Some authorities have suggested that a relative risk greater than 2.0 meets the standard of proof for specific causation. Such a definite criterion is appealing, but there are di#culties. Bias and confounding are familiar problems; individual di#erences must be considered too. The issues are explored in the context of the swine flu vaccine and Guillain-Barre syndrome. The conclusion: there is a considerable gap between relative risks and proof of specific causation.<F4.051e+05> 1. Introduction<F4.554e+05> In a toxic tort case, the plainti# is exposed to a toxic agent, su#ers injury, and sues. To win, the plainti# must prove (i) "general causation" (the agent is capable of producing th...

ABSTRACT The standard approach to analyzing randomized trials ignores information on postrandomization compliance. Application of these methods results in estimates that may lack the desired causal interpretation. We employ a new method of estimation and analyze data from the Multiple Risk Factor Intervention Trial (MRFIT) to estimate the causal effect of quitting cigarette smoking. Our procedure utilizes a method proposed by Robins and Tsiatis and allows us to take advantage of postrandomization smoking history without requiring untenable assumptions about the comparability of compliers and noncompliers. We contrast the performance of our method and the standard intent-to-treat analysis in the MRFIT data and in simulated data in which compliance rates are varied.

Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by “sophisticated” models. This article discusses current models for causation, as applied to experimental and observational data. The intention-to-treat principle and the effect of treatment on the treated will also be discussed. Flaws in per-protocol and treatment-received estimates will be demonstrated.

This paper provides a conceptual introduction to causal inference, aimed to assist health services researchers benefit from recent advances in this area. The paper stresses 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, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, corrections for noncompliance, and a symbiosis between counterfactual and graphical methods of analysis.

This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on non-parametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 1950-60s, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and non-linear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and

Abstract not found