Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment Under Unconfoundedness

An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These effects provide an intuitive decomposition of the total effect that is useful for policy purposes. We offer identification conditions based on an unconfoundedness assumption to estimate them, within a heterogeneous effect environment, and for the cases of a randomly assigned treatment and when selection into the treatment is based on observables. Two empirical applications illustrate the concepts and methods.

• Publication year

  2009

• Venue

  Social Science Research Network

• Publication date

  2009-06-21

• Fields of study

  Economics, Psychology

• Identifiers
  DOI 10.2139/ssrn.1423353
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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