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Integrating and Generalizing Causal Estimates

In: Demystifying Causal Inference

Author

Listed:
  • Vikram Dayal

    (Institute of Economic Growth)

  • Anand Murugesan

    (Central European University)

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.

Suggested Citation

  • Vikram Dayal & Anand Murugesan, 2023. "Integrating and Generalizing Causal Estimates," Springer Books, in: Demystifying Causal Inference, chapter 0, pages 255-294, Springer.
    • Handle: RePEc:spr:sprchp:978-981-99-3905-3_12
    DOI: 10.1007/978-981-99-3905-3_12
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