Design

How the study population and the comparison are assembled, before any modeling begins.

  • Randomized designs (RCTs, cluster, stepped-wedge)
  • Self-controlled designs (case-crossover, SCCS)
  • Target trial emulation

Identification

The assumption that licenses reading a causal effect out of observational data.

  • Back-door Adjustment (conditional exchangeability)
  • Difference-in-Differences
  • Front-door Adjustment
  • Instrumental Variables
  • Interrupted Time Series
  • Regression Discontinuity
  • Synthetic Controls

Estimation

How the identified quantity is computed once those assumptions are in place.

  • Augmented Inverse Probability Weighting (AIPW)
  • Bayesian Structural Time Series
  • G-computation
  • Inverse Probability of Treatment Weighting (IPTW)
  • Marginal Structural Models
  • Matching
  • Propensity Scores (the shared ingredient in matching, IPTW, and AIPW)
  • Targeted Maximum Likelihood Estimate (TMLE)

Sensitivity

What happens to the conclusion when those assumptions fail.