Can we do reliable causal inference from observational data?

Randomised experiments are the gold standard for causal claims but are often infeasible. Methods such as instrumental variables, propensity scores, regression discontinuity and structural causal models attempt to recover causal effects from observational data.

Each method makes strong assumptions that are usually not testable from data. How to bound and report causal uncertainty robustly remains an active methodological frontier.