1.4 Motivation: The causal inference ‘revolution’

  • Revolution of identification (K. Imai 2011; Keele 2015a, 102)1
  • Revolution of potential outcomes (e.g. Rubin 1974)2
  • New Style of writing/conducting data analysis
  • New rationales for evaluating research (Causal empiricism)
  • Impact on research questions3
  • Journal of Causal Inference4
  • Exciting ongoing debates (e.g., on RCTs)5
  • Methodological socialization across generations

Notes

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References

Imai, Kosuke. 2011. “Introduction to the Virtual Issue: Past and Future Research Agenda on Causal Inference.” Political Analysis.

Keele, Luke. 2015a. “The Discipline of Identification.” PS Polit. Sci. Polit. 48 (01): 102–6.

Rubin, Donald B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” J. Educ. Psychol. 66 (5): 688–701.


  1. Focuses on what part of an association (e.g. a regression coefficient or a correlation) can really be attributed to the causal effect of D on Y. And, what assumptions we need to make to reduce an association to its causal component.

  2. Classically used methods (e.g. OLS regression) are reinterpreted departing from the framework of potential outcomes.

  3. Focus on causal inference has triggered certain trends, e.g. searching for exogenous variation, e.g. weather, natural disasters, historical factors etc. Once a particular design becomes popular, researchers tend to copy and hunt for use-cases of that design (e.g. regression discontinuity design).

  4. Existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies […] JCI highlights both the uniqueness and interdisciplinary nature of causal research” (The editors)

  5. See for instance the debate on the value of randomized control trials (e.g. see Cuesta and Imai 2016; Deaton 2010; Imbens 2010). See videos 1, 2 and 3.