Inferring causal relationships in observational data is crucial for understanding scientific and social processes. We develop the first statistical machine learning approach for automatically discovering regression discontinuity designs (RDDs), a quasi-experimental setup often used in econometrics. Our method identifies interpretable, localized RDDs in arbitrary dimensional data and can seamlessly compute treatment effects without expert supervision. By applying the technique to a variety of synthetic and real datasets, we demonstrate robust performance under adverse conditions including unobserved variables, substantial noise, and model misspecification.
|Original language||English (US)|
|Title of host publication||KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||9|
|State||Published - Jul 19 2018|
|Event||24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom|
Duration: Aug 19 2018 → Aug 23 2018
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Other||24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018|
|Period||8/19/18 → 8/23/18|
Bibliographical notePublisher Copyright:
© 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
- Natural experiments
- Pattern detection
- Regression discontinuity