Download Counterfactuals and Causal Inference: Methods and Principles by Stephen L. Morgan PDF

By Stephen L. Morgan

During this moment version of Counterfactuals and Causal Inference, thoroughly revised and accelerated, the fundamental positive aspects of the counterfactual method of observational information research are provided with examples from the social, demographic, and well-being sciences. replacement estimation strategies are first brought utilizing either the capability final result version and causal graphs; and then, conditioning concepts, reminiscent of matching and regression, are awarded from a possible results standpoint. For learn situations during which vital determinants of causal publicity are unobserved, substitute concepts, similar to instrumental variable estimators, longitudinal equipment, and estimation through causal mechanisms, are then provided. the significance of causal impact heterogeneity is under pressure through the publication, and the necessity for deep causal clarification through mechanisms is mentioned.

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16 Both of these back-door paths can be blocked in order to eliminate the supplemental noncausal association between D and Y by observing and then conditioning on A and B or by observing and then conditioning on F . These two conditioning strategies are general in the sense that they will succeed in producing consistent causal effect estimates of the effect of D on Y under a variety of conditioning techniques and in the presence of nonlinear effects. 2). 16 As we note later in Chapter 3 when more formally defining back-door paths, the two paths labeled “back-door paths” in the main text here may represent many back-door paths because the bidirected edges may represent more than one common cause of the variables they point to.

4 Observational Data and Random-Sample Surveys When we discuss methods and examples throughout this book, we will usually assume that the data have been generated by a relatively large random-sample survey. We will also assume that the proportion and pattern of individuals who are exposed to the cause are fixed in the population by whatever process generates causal exposure. We rely on the random-sample perspective because we feel it is the most natural framing of these methods for the typical social scientist, even though many of the classic applications and early methodological pieces in this literature do not reference random-sample surveys.

Wand et al. (2001) published a refined version of their analysis that spread like wildfire on the Internet in the week following the presidential election. They asserted that . . the butterfly ballot used in Palm Beach County, Florida, in the 2000 presidential election caused more than 2,000 Democratic voters to vote by mistake for Reform candidate Pat Buchanan, a number larger than George W. Bush’s certified margin of victory in Florida. (Wand et al. 4. Observational Data and Random-Sample Surveys 21 Reflecting on efforts to recount votes undertaken by various media outlets, Wand and his colleagues identify the crucial contribution of their analysis: Our analysis answers a counterfactual question about voter intentions that such investigations [by media outlets of votes cast] cannot resolve.

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