Average treatment effect (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Average treatment effect" in English language version.

refsWebsite

Global rank English rank

6doi.org

2^nd place

4arxiv.org

69^th place

59^th place

3nih.gov

4^th place

2semanticscholar.org

11^th place

8^th place

2mlr.press

low place

2harvard.edu

18^th place

17^th place

1jstor.org

26^th place

20^th place

1markhw.com

low place

1neurips.cc

low place

arxiv.org

Wager, Stefan; Athey, Susan (2015). "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests". arXiv:1510.04342 [stat.ME].
Markham, Alex; Das, Richeek; Grosse-Wentrup, Moritz (2022). "A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations". Proc. CLeaR. PMLR 177: 542–558. arXiv:2106.03480.
Nie, Xinkun; Wager, Stefan (2021). "Quasi-oracle estimation of heterogeneous treatment effects". Biometrika. 108 (2): 299–319. arXiv:1712.04912. doi:10.1093/biomet/asaa076.
Burkhart, Michael C.; Ruiz, Gabriel (2022). "Neuroevolutionary Feature Representations for Causal Inference". Computational Science – ICCS 2022. Lecture Notes in Computer Science. Vol. 13351. pp. 3–10. arXiv:2205.10541. doi:10.1007/978-3-031-08754-7_1. ISBN 978-3-031-08753-0. S2CID 248987304.

doi.org

Zhao, Q., & Hastie, T. (2019). Causal Interpretations of Black-Box Models. Journal of Business & Economic Statistics, 39(1), 272–281. https://doi.org/10.1080/07350015.2019.1624293 online ncbi/
Holland, Paul W. (1986). "Statistics and Causal Inference". J. Amer. Statist. Assoc. 81 (396): 945–960. doi:10.1080/01621459.1986.10478354. JSTOR 2289064.
Imai, Kosuke; King, Gary; Stuart, Elizabeth A. (2008). "Misunderstandings Between Experimentalists and Observationalists About Causal Inference". J. R. Stat. Soc. Ser. A. 171 (2): 481–502. doi:10.1111/j.1467-985X.2007.00527.x. S2CID 17852724.
Nie, Xinkun; Wager, Stefan (2021). "Quasi-oracle estimation of heterogeneous treatment effects". Biometrika. 108 (2): 299–319. arXiv:1712.04912. doi:10.1093/biomet/asaa076.
Künzel, Sören; Sekhon, Jasjeet; Bickel, Peter; Yu, Bin (2019). "Metalearners for estimating heterogeneous treatment effects using machine learning". Proceedings of the National Academy of Sciences. 116 (10): 4156–4165. Bibcode:2019PNAS..116.4156K. doi:10.1073/pnas.1804597116. PMC 6410831. PMID 30770453.
Burkhart, Michael C.; Ruiz, Gabriel (2022). "Neuroevolutionary Feature Representations for Causal Inference". Computational Science – ICCS 2022. Lecture Notes in Computer Science. Vol. 13351. pp. 3–10. arXiv:2205.10541. doi:10.1007/978-3-031-08754-7_1. ISBN 978-3-031-08753-0. S2CID 248987304.

harvard.edu

nrs.harvard.edu

Imai, Kosuke; King, Gary; Stuart, Elizabeth A. (2008). "Misunderstandings Between Experimentalists and Observationalists About Causal Inference". J. R. Stat. Soc. Ser. A. 171 (2): 481–502. doi:10.1111/j.1467-985X.2007.00527.x. S2CID 17852724.

ui.adsabs.harvard.edu

Künzel, Sören; Sekhon, Jasjeet; Bickel, Peter; Yu, Bin (2019). "Metalearners for estimating heterogeneous treatment effects using machine learning". Proceedings of the National Academy of Sciences. 116 (10): 4156–4165. Bibcode:2019PNAS..116.4156K. doi:10.1073/pnas.1804597116. PMC 6410831. PMID 30770453.

jstor.org

Holland, Paul W. (1986). "Statistics and Causal Inference". J. Amer. Statist. Assoc. 81 (396): 945–960. doi:10.1080/01621459.1986.10478354. JSTOR 2289064.

markhw.com

"Explicitly Optimizing on Causal Effects via the Causal Random Forest: A Practical Introduction and Tutorial". 14 October 2018.

mlr.press

proceedings.mlr.press

Markham, Alex; Das, Richeek; Grosse-Wentrup, Moritz (2022). "A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations". Proc. CLeaR. PMLR 177: 542–558. arXiv:2106.03480.
Johansson, Fredrik; Shalit, Uri; Sontag, David (2016). "Learning Representations for Counterfactual Inference". Proc. ICML. PMLR 48: 3020–3029.

neurips.cc

proceedings.neurips.cc

Huang, Biwei; Zhang, Kun; Xie, Pengtao; Gong, Mingming; Xing, Eric P.; Glymour, Clark (2019). "Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering". Advances in Neural Information Processing Systems (NeurIPS). 32.

nih.gov

ncbi.nlm.nih.gov

Zhao, Q., & Hastie, T. (2019). Causal Interpretations of Black-Box Models. Journal of Business & Economic Statistics, 39(1), 272–281. https://doi.org/10.1080/07350015.2019.1624293 online ncbi/
Künzel, Sören; Sekhon, Jasjeet; Bickel, Peter; Yu, Bin (2019). "Metalearners for estimating heterogeneous treatment effects using machine learning". Proceedings of the National Academy of Sciences. 116 (10): 4156–4165. Bibcode:2019PNAS..116.4156K. doi:10.1073/pnas.1804597116. PMC 6410831. PMID 30770453.

pubmed.ncbi.nlm.nih.gov

Künzel, Sören; Sekhon, Jasjeet; Bickel, Peter; Yu, Bin (2019). "Metalearners for estimating heterogeneous treatment effects using machine learning". Proceedings of the National Academy of Sciences. 116 (10): 4156–4165. Bibcode:2019PNAS..116.4156K. doi:10.1073/pnas.1804597116. PMC 6410831. PMID 30770453.

semanticscholar.org

api.semanticscholar.org

Imai, Kosuke; King, Gary; Stuart, Elizabeth A. (2008). "Misunderstandings Between Experimentalists and Observationalists About Causal Inference". J. R. Stat. Soc. Ser. A. 171 (2): 481–502. doi:10.1111/j.1467-985X.2007.00527.x. S2CID 17852724.
Burkhart, Michael C.; Ruiz, Gabriel (2022). "Neuroevolutionary Feature Representations for Causal Inference". Computational Science – ICCS 2022. Lecture Notes in Computer Science. Vol. 13351. pp. 3–10. arXiv:2205.10541. doi:10.1007/978-3-031-08754-7_1. ISBN 978-3-031-08753-0. S2CID 248987304.