Automated local regression discontinuity design discovery

William Herlands; Andrew Gordon Wilson; Edward McFowland; Daniel B. Neill

doi:10.1145/3219819.3219982

Automated local regression discontinuity design discovery

William Herlands, Andrew Gordon Wilson, Edward McFowland, Daniel B. Neill

Information and Decision Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

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
Pages	1512-1520
Number of pages	9
ISBN (Print)	9781450355520
DOIs	https://doi.org/10.1145/3219819.3219982
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

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Country/Territory	United Kingdom
City	London
Period	8/19/18 → 8/23/18

Bibliographical note

Publisher Copyright:
© 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.

Keywords

Natural experiments
Pattern detection
Regression discontinuity

Access

10.1145/3219819.3219982

OpenUrl availability

Full text

Cite this

Herlands, W., Wilson, A. G., McFowland, E., & Neill, D. B. (2018). Automated local regression discontinuity design discovery. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1512-1520). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3219819.3219982

Automated local regression discontinuity design discovery. / Herlands, William; Wilson, Andrew Gordon; McFowland, Edward et al.
KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. p. 1512-1520 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Herlands, W, Wilson, AG, McFowland, E & Neill, DB 2018, Automated local regression discontinuity design discovery. in KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 1512-1520, 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 8/19/18. https://doi.org/10.1145/3219819.3219982

Herlands W, Wilson AG, McFowland E, Neill DB. Automated local regression discontinuity design discovery. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2018. p. 1512-1520. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3219819.3219982

Herlands, William ; Wilson, Andrew Gordon ; McFowland, Edward et al. / Automated local regression discontinuity design discovery. KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. pp. 1512-1520 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{09608bcff61444bfb7bce2a29cf54325,

title = "Automated local regression discontinuity design discovery",

abstract = "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.",

keywords = "Natural experiments, Pattern detection, Regression discontinuity",

author = "William Herlands and Wilson, {Andrew Gordon} and Edward McFowland and Neill, {Daniel B.}",

note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.; 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 ; Conference date: 19-08-2018 Through 23-08-2018",

year = "2018",

month = jul,

day = "19",

doi = "10.1145/3219819.3219982",

language = "English (US)",

isbn = "9781450355520",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery",

pages = "1512--1520",

booktitle = "KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

}

TY - GEN

T1 - Automated local regression discontinuity design discovery

AU - Herlands, William

AU - Wilson, Andrew Gordon

AU - McFowland, Edward

AU - Neill, Daniel B.

PY - 2018/7/19

Y1 - 2018/7/19

N2 - 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.

AB - 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.

KW - Natural experiments

KW - Pattern detection

KW - Regression discontinuity

UR - http://www.scopus.com/inward/record.url?scp=85051562408&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051562408&partnerID=8YFLogxK

U2 - 10.1145/3219819.3219982

DO - 10.1145/3219819.3219982

M3 - Conference contribution

AN - SCOPUS:85051562408

SN - 9781450355520

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 1512

EP - 1520

BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

T2 - 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018

Y2 - 19 August 2018 through 23 August 2018

ER -

Automated local regression discontinuity design discovery

Abstract

Publication series

Other

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this