Matching

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

Chapter Chapter 7 in Demystifying Causal Inference, 2023, pp 109-134 from Springer

Abstract: Abstract Matching is a method to uncover ‘hidden experiments’ in observational data, as conceived by King and Nielsen (Polit Anal 27(4):435–454, 2019). It involves processing observational data to create a comparison group where treated and control units are similar on observed characteristics. Advocates of matching argue that creating observably similar groups allows for an apples-to-apples comparison, which can be used to estimate the causal effect of a treatment, be it an intervention, a policy, or a program. Matching is an appealing method to estimate causal effects as it offers the promise of using readily available observational data, without the need for randomized experiments, which can be expensive and time-consuming. In this chapter, we will explore how matching methods can deliver on this promise of finding causal effects from non-experimental data.

Keywords: Matching; coarsened exact matching; propensity score matching; Mahalanobis distance matching; genetic matching; model dependence (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_7

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DOI: 10.1007/978-981-99-3905-3_7

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