add links to papers. remove sample posts.

Author: jeongyoonlee

_notebooks/2020-02-20-test.ipynb

@@ -83,28 +83,28 @@
 
 ### References
 
-1. Künzel, Sören R., et al. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the national academy of sciences 116.10 (2019): 4156-4165. (paper)
-2. Chernozhukov, Victor, et al. "Double/debiased/neyman machine learning of treatment effects." American Economic Review 107.5 (2017): 261-65. (paper)
-3. Nie, Xinkun, and Stefan Wager. "Quasi-oracle estimation of heterogeneous treatment effects." arXiv preprint arXiv:1712.04912 (2017). (paper)
-4. Tso, Fung Po, et al. "DragonNet: a robust mobile internet service system for long-distance trains." IEEE transactions on mobile computing 12.11 (2013): 2206-2218. (paper)
-5. Louizos, Christos, et al. "Causal effect inference with deep latent-variable models." arXiv preprint arXiv:1705.08821 (2017). (paper)
-6. Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association 113.523 (2018): 1228-1242. (paper)
-7. Oprescu, Miruna, et al. "EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects." (repo)
-8. Chen, Huigang, et al. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020). (repo)
-9. Yao, Liuyi, et al. "A survey on causal inference." arXiv preprint arXiv:2002.02770 (2020). (paper)
-10. Goldenberg, Dmitri, et al. "Personalization in Practice: Methods and Applications." Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021. (paper)
-11. Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. (paper)
-12. Athey, Susan, and Stefan Wager. "Efficient policy learning." arXiv preprint arXiv:1702.02896 (2017). (paper)
-13. Sharma, Amit, and Emre Kiciman. "Causal Inference and Counterfactual Reasoning." Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. 2020. 369-370. (paper)
-14. Li, Ang, and Judea Pearl. "Unit selection based on counterfactual logic." Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 2019. (paper)
-15. Kennedy, Edward H. "Optimal doubly robust estimation of heterogeneous causal effects." arXiv preprint arXiv:2004.14497 (2020). (paper)
-16. Gruber, Susan, and Mark J. Van Der Laan. "Targeted maximum likelihood estimation: A gentle introduction." (2009). (paper)
-17. D. Foster, V. Syrgkanis. Orthogonal Statistical Learning. Proceedings of the 32nd Annual Conference on Learning Theory (COLT), 2019
-18. V. Syrgkanis, V. Lei, M. Oprescu, M. Hei, K. Battocchi, G. Lewis. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019
-19. M. Oprescu, V. Syrgkanis and Z. S. Wu. Orthogonal Random Forest for Causal Inference. Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.
-20. Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. Deep IV: A flexible approach for counterfactual prediction. Proceedings of the 34th International Conference on Machine Learning, ICML'17, 2017.
-21. Battocchi, K., Dillon, E., Hei, M., Lewis, G., Oprescu, M., & Syrgkanis, V. (2021). Estimating the Long-Term Effects of Novel Treatments. arXiv preprint arXiv:2103.08390.
-22. Lewis, G., & Syrgkanis, V. (2020). Double/Debiased Machine Learning for Dynamic Treatment Effects. arXiv preprint arXiv:2002.07285.
+1. Künzel, Sören R., et al. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the national academy of sciences 116.10 (2019): 4156-4165. ([paper](https://www.pnas.org/content/pnas/116/10/4156.full.pdf))
+2. Chernozhukov, Victor, et al. "Double/debiased/neyman machine learning of treatment effects." American Economic Review 107.5 (2017): 261-65. ([paper](https://arxiv.org/pdf/1701.08687))
+3. Nie, Xinkun, and Stefan Wager. "Quasi-oracle estimation of heterogeneous treatment effects." arXiv preprint arXiv:1712.04912 (2017) ([paper](https://arxiv.org/pdf/1712.04912))
+4. Tso, Fung Po, et al. "DragonNet: a robust mobile internet service system for long-distance trains." IEEE transactions on mobile computing 12.11 (2013): 2206-2218. ([paper](https://eprints.gla.ac.uk/56409/1/56409.pdf))
+5. Louizos, Christos, et al. "Causal effect inference with deep latent-variable models." arXiv preprint arXiv:1705.08821 (2017) ([paper](https://arxiv.org/pdf/1705.08821))
+6. Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association 113.523 (2018): 1228-1242. ([paper](https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1319839))
+7. Oprescu, Miruna, et al. "EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects." ([repo](https://github.com/microsoft/EconML))
+8. Chen, Huigang, et al. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020) ([repo](https://github.com/uber/causalml))
+9. Yao, Liuyi, et al. "A survey on causal inference." arXiv preprint arXiv:2002.02770 (2020). ([paper](https://arxiv.org/pdf/2002.02770.pdf))
+10. Goldenberg, Dmitri, et al. "Personalization in Practice: Methods and Applications." Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021 ([paper](https://drive.google.com/drive/folders/1c_khoTDRbkoRY5OiaxEfUxRQkyNv3FeK))
+11. Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. ([paper](https://www.cambridge.org/core/journals/political-analysis/article/selection-bias-approach-to-sensitivity-analysis-for-causal-effects/788C169FAF5482452566811136D4F9B4))
+12. Athey, Susan, and Stefan Wager. "Efficient policy learning." arXiv preprint arXiv:1702.02896 (2017). ([paper](https://arxiv.org/pdf/1702.02896.pdf))
+13. Sharma, Amit, and Emre Kiciman. "Causal Inference and Counterfactual Reasoning." Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. 2020. 369-370. ([paper](https://dl.acm.org/doi/abs/10.1145/3371158.3371231))
+14. Li, Ang, and Judea Pearl. "Unit selection based on counterfactual logic." Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 2019 ([paper](https://par.nsf.gov/biblio/10180278))
+15. Kennedy, Edward H. "Optimal doubly robust estimation of heterogeneous causal effects." arXiv preprint arXiv:2004.14497 (2020) ([paper](https://arxiv.org/pdf/2004.14497.pdf))
+16. Gruber, Susan, and Mark J. Van Der Laan. "Targeted maximum likelihood estimation: A gentle introduction." (2009) ([paper](https://biostats.bepress.com/cgi/viewcontent.cgi?article=1255&context=ucbbiostat))
+17. D. Foster, V. Syrgkanis. Orthogonal Statistical Learning. Proceedings of the 32nd Annual Conference on Learning Theory (COLT), 2019 ([paper](https://arxiv.org/pdf/1901.09036.pdf))
+18. V. Syrgkanis, V. Lei, M. Oprescu, M. Hei, K. Battocchi, G. Lewis. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019 ([paper](https://arxiv.org/pdf/1905.10176.pdf))
+19. M. Oprescu, V. Syrgkanis and Z. S. Wu. Orthogonal Random Forest for Causal Inference. Proceedings of the 36th International Conference on Machine Learning (ICML), 2019 ([paper](http://proceedings.mlr.press/v97/oprescu19a/oprescu19a.pdf))
+20. Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. Deep IV: A flexible approach for counterfactual prediction. Proceedings of the 34th International Conference on Machine Learning, ICML'17, 2017 ([paper](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf))
+21. Battocchi, K., Dillon, E., Hei, M., Lewis, G., Oprescu, M., & Syrgkanis, V. (2021). Estimating the Long-Term Effects of Novel Treatments. arXiv preprint arXiv:2103.08390. ([paper](https://arxiv.org/pdf/2103.08390.pdf))
+22. Lewis, G., & Syrgkanis, V. (2020). Double/Debiased Machine Learning for Dynamic Treatment Effects. arXiv preprint arXiv:2002.07285. ([paper](https://arxiv.org/pdf/2002.07285.pdf))
 
 
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