4.30 Summary

  • Causal/treatment effect is defined in terms of potential outcomes
    • Same unit, same moment in time post-treatment
  • Fundamental problem: Only one potential outcome is observable!
  • Problem of causal inference is a missing data problem
  • Conceptualize potential outcomes as having trajectories over time
  • Estimation requires filling in missing outcome(s)
    • Scientific solutions:
      • Estimate individual causal (treatment) effects
      • Compare with different unit or earlier/later outcome of same unit
      • Assumptions: unit homogeneity, temporal stability, causal transience
    • Statistical solution:
      • e.g., randomization
      • Estimate average causal (treatment) effect
      • Compare averages between treatment and control groups
      • Assumptions: IA, SUTVA
  • Framework extendable to whatever units (countries, towns, physial objects) and treatments with several levels
  • Conceptualize treatment variable as an intervention/action
    • …makes alternative values and timing clearer! (e.g. “going to college”)