Update README.md

added dragonnet

README.md

@@ -28,14 +28,15 @@ Please cite [our survey paper](https://arxiv.org/pdf/1809.09337) if this index i
 |TMLE|[Gruber, Susan, and Mark J. van der Laan. "tmle: An R package for targeted maximum likelihood estimation." (2011).](https://www.jstatsoft.org/article/view/v051i13)|[R](https://cran.r-project.org/web/packages/tmle/index.html)|
 |---|---|---|
 |BNN, BLR|[Johansson, Fredrik, Uri Shalit, and David Sontag. "Learning representations for counterfactual inference." In International Conference on Machine Learning, pp. 3020-3029. 2016.](http://www.jmlr.org/proceedings/papers/v48/johansson16.pdf)|[Python](https://github.com/oddrose/cfrnet)|
-|Tarnet, Counterfactual Regression|[Shalit, Uri, Fredrik D. Johansson, and David Sontag. "Estimating individual treatment effect: generalization bounds and algorithms." arXiv preprint arXiv:1606.03976 (2016).](https://arxiv.org/pdf/1606.03976)|[Python](https://github.com/oddrose/cfrnet)|
+|TARNet, Counterfactual Regression|[Shalit, Uri, Fredrik D. Johansson, and David Sontag. "Estimating individual treatment effect: generalization bounds and algorithms." arXiv preprint arXiv:1606.03976 (2016).](https://arxiv.org/pdf/1606.03976)|[Python](https://github.com/oddrose/cfrnet)|
 |Causal Effect VAE|[Louizos, Christos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, and Max Welling. "Causal effect inference with deep latent-variable models." In Advances in Neural Information Processing Systems, pp. 6446-6456. 2017.](http://papers.nips.cc/paper/7223-causal-effect-inference-with-deep-latent-variable-models.pdf)|[Python](https://github.com/AMLab-Amsterdam/CEVAE)|
 |SITE|[Yao, Liuyi, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. "Representation Learning for Treatment Effect Estimation from Observational Data." In Advances in Neural Information Processing Systems, pp. 2638-2648. 2018.](https://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data.pdf)|[Python](https://github.com/Osier-Yi/SITE)|
 |Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning|[Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. "Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning." arXiv preprint arXiv:1706.03461 (2017).](https://www.pnas.org/content/pnas/early/2019/02/14/1804597116.full.pdf)|[R](https://github.com/soerenkuenzel/hte)|
 |Causal Forest|[Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association just-accepted (2017).](https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1319839)|[R](https://github.com/grf-labs/grf) [Python](https://github.com/kjung/scikit-learn)|
 |Bayesian Additive Regression Trees (BART)|[Hill, Jennifer L. "Bayesian nonparametric modeling for causal inference." Journal of Computational and Graphical Statistics 20, no. 1 (2011): 217-240.](https://www.tandfonline.com/doi/pdf/10.1198/jcgs.2010.08162)|[Python](https://github.com/JakeColtman/bartpy)|
 |GANITE|[Yoon, Jinsung, James Jordon, and Mihaela van der Schaar. "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets." (2018).](https://openreview.net/forum?id=ByKWUeWA-)|[Python](https://github.com/d909b/perfect_match/tree/master/perfect_match/models/baselines/ganite_package)|
 |Perfect Match|[Schwab, Patrick, Lorenz Linhardt, and Walter Karlen. "Perfect match: A simple method for learning representations for counterfactual inference with neural networks." arXiv preprint arXiv:1810.00656 (2018)](https://arxiv.org/pdf/1810.00656)|[Python](https://github.com/d909b/perfect_match)|
+|Dragonnet|[Adapting Neural Networks for the Estimation of Treatment Effects](https://arxiv.org/abs/1906.02120)|[Python](https://github.com/claudiashi57/dragonnet)|
 |Active Learning for Decision-Making from Imbalanced Observational Data|[Active Learning for Decision-Making from Imbalanced Observational Data](https://arxiv.org/abs/1904.05268)|NA|
 |ABCEI|[Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data](https://arxiv.org/pdf/1904.13335.pdf)|NA|
 |NSGP (Non-stationary Gaussian Process Prior)|[Alaa, Ahmed, and Mihaela Schaar. "Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design." In International Conference on Machine Learning, pp. 129-138. 2018.](http://proceedings.mlr.press/v80/alaa18a/alaa18a.pdf)|NA|