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Data-Adaptive Causal Effects and Superefficiency
| Content Provider | Scilit |
|---|---|
| Author | Aronow, Peter M. |
| Copyright Year | 2016 |
| Abstract | Recent approaches in causal inference have proposed estimating average causal effects that are local to some subpopulation, often for reasons of efficiency. These inferential targets are sometimes data-adaptive, in that they are dependent on the empirical distribution of the data. In this short note, we show that if researchers are willing to adapt the inferential target on the basis of efficiency, then extraordinary gains in precision can potentially be obtained. Specifically, when causal effects are heterogeneous, any asymptotically normal and root- n $n$ consistent estimator of the population average causal effect is superefficient for a data-adaptive local average causal effect. |
| Related Links | http://www.degruyter.com/downloadpdf/j/jci.2016.4.issue-2/jci-2016-0007/jci-2016-0007.xml |
| ISSN | 21933677 |
| e-ISSN | 21933685 |
| DOI | 10.1515/jci-2016-0007 |
| Journal | Journal of Causal Inference |
| Issue Number | 2 |
| Volume Number | 4 |
| Language | English |
| Publisher | Walter de Gruyter GmbH |
| Publisher Date | 2016-09-01 |
| Access Restriction | Open |
| Subject Keyword | Journal of Causal Inference Statistics and Probability Causal Inference Superefficiency Data-adaptive Target Parameter Local Average Treatment Effect Journal: Journal of Causal Inference, Vol- 4, Issue- 1 |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Statistics, Probability and Uncertainty |
