Abstract
Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However, this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend. Relatedly, researchers advocating for new methods typically compare their method to at best 2 or 3 other causal inference strategies and test using simulations that may or may not be designed to equally tease out flaws in all the competing methods. The causal inference data analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both of these issues. The researchers creating the data testing grounds were distinct from the researchers submitting methods whose efficacy would be evaluated. Results from 30 competitors across the two versions of the competition (black-box algorithms and do-it-yourself analyses) are presented along with post-hoc analyses that reveal information about the characteristics of causal inference strategies and settings that affect performance. The most consistent conclusion was that methods that flexibly model the response surface perform better overall than methods that fail to do so. Finally new methods are proposed that combine features of several of the top-performing submitted methods.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 43-68 |
| Number of pages | 26 |
| Journal | Statistical Science |
| Volume | 34 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 1 2019 |
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Keywords
- Automated algorithms
- Causal inference
- Competition
- Evaluation
- Machine learning
ASJC Scopus subject areas
- Statistics and Probability
- Mathematics(all)
- Statistics, Probability and Uncertainty
Cite this
Automated versus do-it-yourself methods for causal inference : Lessons learned from a data analysis competition. / Dorie, Vincent; Hill, Jennifer; Shalit, Uri; Scott, Marc; Cervone, Dan.
In: Statistical Science, Vol. 34, No. 1, 01.02.2019, p. 43-68.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Automated versus do-it-yourself methods for causal inference
T2 - Lessons learned from a data analysis competition
AU - Dorie, Vincent
AU - Hill, Jennifer
AU - Shalit, Uri
AU - Scott, Marc
AU - Cervone, Dan
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However, this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend. Relatedly, researchers advocating for new methods typically compare their method to at best 2 or 3 other causal inference strategies and test using simulations that may or may not be designed to equally tease out flaws in all the competing methods. The causal inference data analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both of these issues. The researchers creating the data testing grounds were distinct from the researchers submitting methods whose efficacy would be evaluated. Results from 30 competitors across the two versions of the competition (black-box algorithms and do-it-yourself analyses) are presented along with post-hoc analyses that reveal information about the characteristics of causal inference strategies and settings that affect performance. The most consistent conclusion was that methods that flexibly model the response surface perform better overall than methods that fail to do so. Finally new methods are proposed that combine features of several of the top-performing submitted methods.
AB - Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However, this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend. Relatedly, researchers advocating for new methods typically compare their method to at best 2 or 3 other causal inference strategies and test using simulations that may or may not be designed to equally tease out flaws in all the competing methods. The causal inference data analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both of these issues. The researchers creating the data testing grounds were distinct from the researchers submitting methods whose efficacy would be evaluated. Results from 30 competitors across the two versions of the competition (black-box algorithms and do-it-yourself analyses) are presented along with post-hoc analyses that reveal information about the characteristics of causal inference strategies and settings that affect performance. The most consistent conclusion was that methods that flexibly model the response surface perform better overall than methods that fail to do so. Finally new methods are proposed that combine features of several of the top-performing submitted methods.
KW - Automated algorithms
KW - Causal inference
KW - Competition
KW - Evaluation
KW - Machine learning
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U2 - 10.1214/18-STS667
DO - 10.1214/18-STS667
M3 - Article
VL - 34
SP - 43
EP - 68
JO - Statistical Science
JF - Statistical Science
SN - 0883-4237
IS - 1
ER -