Placebo topography: A unified robustness map for natural experiments

Build a diagnostic framework that systematically maps false-positive risk by probing dozens of placebo cutoffs (RD), null event times (ITS/event study), and alternative donor pools (synthetic control), alongside algorithmic choices in DML/DID. The output is a topographic robustness map showing where an effect survives counterfactual perturbations. This unified tool quantifies and visualizes robustness across multiple axes simultaneously, turning robustness checks from narrative paragraphs into reproducible, comparable objects. It incorporates falsification logic, synthetic control assumptions, and DML explainability gaps, and can add spatial spillover placebo permutations. This approach provides early warnings when effects align with seasonal cycles, spatial diffusion, or algorithmic pre-selection, encouraging method selection based on empirical stability rather than trends. The potential impact is establishing a new norm for reporting robustness in natural experiments, especially in policy evaluation and health/population research requiring rapid but reliable inference.

References:

1. The Explaimability of Double Machine Learning Causal Inference in Quasi-Natural Experiments—A Study Based on County Panel Sample Data. Zongxuan Chai, Tingting Zheng (2023). Automated Machine Learning.
2. A blueprint for synthetic control methodology: a causal inference tool for evaluating natural experiments in population health. Ben Barr, Xingna Zhang, M. Green, I. Buchan (2022). British medical journal.