De-biased Machine Learning in Instrumental Variable Models for Treatment Effects
We introduce a de-biased machine learning (DML) approach to estimating complier parameters with high-dimensional data. Complier parameters include local average treatment effect, average complier characteristics, and complier counterfactual outcome distributions. In our approach, the de-biasing is i...
| Main Authors: | , |
|---|---|
| Format: | Report |
| Language: | unknown |
| Subjects: | |
| Online Access: | http://arxiv.org/pdf/1909.05244 |
| id |
ftrepec:oai:RePEc:arx:papers:1909.05244 |
|---|---|
| record_format |
openpolar |
| spelling |
ftrepec:oai:RePEc:arx:papers:1909.05244 2024-04-14T08:10:55+00:00 De-biased Machine Learning in Instrumental Variable Models for Treatment Effects Rahul Singh Liyang Sun http://arxiv.org/pdf/1909.05244 unknown http://arxiv.org/pdf/1909.05244 preprint ftrepec 2024-03-19T10:38:04Z We introduce a de-biased machine learning (DML) approach to estimating complier parameters with high-dimensional data. Complier parameters include local average treatment effect, average complier characteristics, and complier counterfactual outcome distributions. In our approach, the de-biasing is itself performed by machine learning, a variant called automatic de-biased machine learning (Auto-DML). By regularizing the balancing weights, it does not require ad hoc trimming or censoring. We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient. We use the new approach to estimate the effect of 401(k) participation on the distribution of net financial assets. Report DML RePEc (Research Papers in Economics) |
| institution |
Open Polar |
| collection |
RePEc (Research Papers in Economics) |
| op_collection_id |
ftrepec |
| language |
unknown |
| description |
We introduce a de-biased machine learning (DML) approach to estimating complier parameters with high-dimensional data. Complier parameters include local average treatment effect, average complier characteristics, and complier counterfactual outcome distributions. In our approach, the de-biasing is itself performed by machine learning, a variant called automatic de-biased machine learning (Auto-DML). By regularizing the balancing weights, it does not require ad hoc trimming or censoring. We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient. We use the new approach to estimate the effect of 401(k) participation on the distribution of net financial assets. |
| format |
Report |
| author |
Rahul Singh Liyang Sun |
| spellingShingle |
Rahul Singh Liyang Sun De-biased Machine Learning in Instrumental Variable Models for Treatment Effects |
| author_facet |
Rahul Singh Liyang Sun |
| author_sort |
Rahul Singh |
| title |
De-biased Machine Learning in Instrumental Variable Models for Treatment Effects |
| title_short |
De-biased Machine Learning in Instrumental Variable Models for Treatment Effects |
| title_full |
De-biased Machine Learning in Instrumental Variable Models for Treatment Effects |
| title_fullStr |
De-biased Machine Learning in Instrumental Variable Models for Treatment Effects |
| title_full_unstemmed |
De-biased Machine Learning in Instrumental Variable Models for Treatment Effects |
| title_sort |
de-biased machine learning in instrumental variable models for treatment effects |
| url |
http://arxiv.org/pdf/1909.05244 |
| genre |
DML |
| genre_facet |
DML |
| op_relation |
http://arxiv.org/pdf/1909.05244 |
| _version_ |
1796308573750296576 |