Integrating and Generalizing Causal Estimates

Vikram Dayal () and Anand Murugesan ()
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Vikram Dayal: Institute of Economic Growth
Anand Murugesan: Central European University

Chapter Chapter 12 in Demystifying Causal Inference, 2023, pp 255-294 from Springer

Abstract: Abstract Researchers around the globe conduct a large number of policy-relevant studies. As a result, the work on integrating and generalizing studies has grown rapidly in recent years. This literature uses diverse approaches, and is published in technical form in journals. In Sect. 12.2, we provide a simplified non-technical overview. In Sect. 12.3, we provide R code for (1) simulation to get a feel for the concepts and (2) estimation with real data. We begin with meta-analysis, which is well-established and widely practiced. We then consider analyses guided by the potential outcomes framework and causal graphs. In this chapter, we consider questions of the following sort: We have some studies that estimate effects. How do we sum them up? We can first examine the estimates (and confidence intervals) produced by the studies. Further, we can use a statistical model to summarize the distribution of the effects estimated in different studies. We have a specific target population in mind. For that target population, what are the possible effects of an intervention? We have some data related to the target population.

Keywords: Meta-analysis; external validity; internal validity; generalizability; transportability; data fusion; dosearch package (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-99-3905-3_12

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DOI: 10.1007/978-981-99-3905-3_12

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