Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction
Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional linear combination of the covariates that is...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2019-04-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2018-0015 |
| _version_ | 1818665126164692992 |
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| author | Luo Wei Wu Wenbo Zhu Yeying |
| author_facet | Luo Wei Wu Wenbo Zhu Yeying |
| author_sort | Luo Wei |
| collection | DOAJ |
| description | Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional linear combination of the covariates that is sufficient to model the regression causal effect. Compared with the existing applications of sufficient dimension reduction in causal inference, our approaches are more efficient in reducing the dimensionality of covariates, and avoid estimating the individual outcome regressions. The proposed approaches can be used in three ways to assist modeling the regression causal effect: to conduct variable selection, to improve the estimation accuracy, and to detect the heterogeneity. Their usefulness are illustrated by both simulation studies and a real data example. |
| first_indexed | 2024-12-17T05:43:40Z |
| format | Article |
| id | doaj.art-538727ae3dfe4c5fade10a46ce3e837c |
| institution | Directory Open Access Journal |
| issn | 2193-3677 2193-3685 |
| language | English |
| last_indexed | 2024-12-17T05:43:40Z |
| publishDate | 2019-04-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Causal Inference |
| spelling | doaj.art-538727ae3dfe4c5fade10a46ce3e837c2022-12-21T22:01:22ZengDe GruyterJournal of Causal Inference2193-36772193-36852019-04-017168870110.1515/jci-2018-0015Learning Heterogeneity in Causal Inference Using Sufficient Dimension ReductionLuo Wei0Wu Wenbo1Zhu Yeying2Center for Data Science, Zhejiang University, Hangzhou, ChinaDepartment of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, United StatesDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, CanadaOften the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional linear combination of the covariates that is sufficient to model the regression causal effect. Compared with the existing applications of sufficient dimension reduction in causal inference, our approaches are more efficient in reducing the dimensionality of covariates, and avoid estimating the individual outcome regressions. The proposed approaches can be used in three ways to assist modeling the regression causal effect: to conduct variable selection, to improve the estimation accuracy, and to detect the heterogeneity. Their usefulness are illustrated by both simulation studies and a real data example.https://doi.org/10.1515/jci-2018-0015central causal effect subspaceconditional causal effectheterogeneityvariable selection |
| spellingShingle | Luo Wei Wu Wenbo Zhu Yeying Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction Journal of Causal Inference central causal effect subspace conditional causal effect heterogeneity variable selection |
| title | Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction |
| title_full | Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction |
| title_fullStr | Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction |
| title_full_unstemmed | Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction |
| title_short | Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction |
| title_sort | learning heterogeneity in causal inference using sufficient dimension reduction |
| topic | central causal effect subspace conditional causal effect heterogeneity variable selection |
| url | https://doi.org/10.1515/jci-2018-0015 |
| work_keys_str_mv | AT luowei learningheterogeneityincausalinferenceusingsufficientdimensionreduction AT wuwenbo learningheterogeneityincausalinferenceusingsufficientdimensionreduction AT zhuyeying learningheterogeneityincausalinferenceusingsufficientdimensionreduction |