%PDF-1.5 167302 within the National Research Program (NRP) 75 "Big Data". [HJ)mD:K`G?/BPWw(a&ggl }[OvP ps@]TZP?x ;_[YN^0'5 (2017).. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. that units with similar covariates xi have similar potential outcomes y. (2016) to enable the simulation of arbitrary numbers of viewing devices. To compute the PEHE, we measure the mean squared error between the true difference in effect y1(n)y0(n), drawn from the noiseless underlying outcome distributions 1 and 0, and the predicted difference in effect ^y1(n)^y0(n) indexed by n over N samples: When the underlying noiseless distributions j are not known, the true difference in effect y1(n)y0(n) can be estimated using the noisy ground truth outcomes yi (Appendix A). Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. (2017) that use different metrics such as the Wasserstein distance. The advantage of matching on the minibatch level, rather than the dataset level Ho etal. For the IHDP and News datasets we respectively used 30 and 10 optimisation runs for each method using randomly selected hyperparameters from predefined ranges (Appendix I). ^mPEHE Learning Decomposed Representation for Counterfactual Inference All rights reserved. Dudk, Miroslav, Langford, John, and Li, Lihong. Domain-adversarial training of neural networks. PSMMI was overfitting to the treated group. A supervised model navely trained to minimise the factual error would overfit to the properties of the treated group, and thus not generalise well to the entire population. This shows that propensity score matching within a batch is indeed effective at improving the training of neural networks for counterfactual inference. Learning disentangled representations for counterfactual regression. We found that NN-PEHE correlates significantly better with the PEHE than MSE (Figure 2). Estimation and inference of heterogeneous treatment effects using Bayesian nonparametric modeling for causal inference. data is confounder identification and balancing. the treatment effect performs better than the state-of-the-art methods on both The variational fair auto encoder. Edit social preview. PM is easy to implement, Pearl, Judea. Brookhart, and Marie Davidian. (2007). We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. xc```b`g`f`` `6+r @0AcSCw-_0 @ LXa>dx6aTglNa i%d5X{985,`Q`~ S 97L?d25h~a ;-dtc 8:NDZ9sUw{wo=s3W9=54r}I$bcg8y7Z{)4#$'ee u?T'PO+!_,zI2Y-Lm47}7"(Dq#^EYWvDV5o^r-*Yt5Pm@Wt>Ks^8$pUD.r#1[Ir In thispaper we propose a method to learn representations suitedfor counterfactual inference, and show its efcacy in bothsimulated and real world tasks. Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. (2017) claimed that the nave approach of appending the treatment index tj may perform poorly if X is high-dimensional, because the influence of tj on the hidden layers may be lost during training. Causal effect inference with deep latent-variable models. In addition, we trained an ablation of PM where we matched on the covariates X (+ on X) directly, if X was low-dimensional (p<200), and on a 50-dimensional representation of X obtained via principal components analysis (PCA), if X was high-dimensional, instead of on the propensity score. DanielE Ho, Kosuke Imai, Gary King, and ElizabethA Stuart. &5mO"}S~2,z3?H BGKxr gOp1b~7Z7A^:12N$PF"=.DTcuT*5(i\C,nZZq+6TR/]FyQo'I)#TFq==UX KgvAZn&W_j3`"e|>n( The set of available treatments can contain two or more treatments. Our experiments aimed to answer the following questions: What is the comparative performance of PM in inferring counterfactual outcomes in the binary and multiple treatment setting compared to existing state-of-the-art methods? https://archive.ics.uci.edu/ml/datasets/bag+of+words. Want to hear about new tools we're making? stream smartphone, tablet, desktop, television or others Johansson etal. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Balancing those Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Notably, PM consistently outperformed both CFRNET, which accounted for covariate imbalances between treatments via regularisation rather than matching, and PSMMI, which accounted for covariate imbalances by preprocessing the entire training set with a matching algorithm Ho etal. Analysis of representations for domain adaptation. Examples of tree-based methods are Bayesian Additive Regression Trees (BART) Chipman etal. Or, have a go at fixing it yourself the renderer is open source! Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. (2) To run the IHDP benchmark, you need to download the raw IHDP data folds as used by Johanson et al. (3). Bottou, Lon, Peters, Jonas, Quinonero-Candela, Joaquin, Charles, Denis X, Chickering, D Max, Portugaly, Elon, Ray, Dipankar, Simard, Patrice, and Snelson, Ed. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. Learning Disentangled Representations for CounterFactual Regression 373 0 obj Similarly, in economics, a potential application would, for example, be to determine how effective certain job programs would be based on results of past job training programs LaLonde (1986). See https://www.r-project.org/ for installation instructions. Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. << /Type /XRef /Length 73 /Filter /FlateDecode /DecodeParms << /Columns 4 /Predictor 12 >> /W [ 1 2 1 ] /Index [ 367 184 ] /Info 183 0 R /Root 369 0 R /Size 551 /Prev 846568 /ID [<6128b543239fbdadfc73903b5348344b>] >> BayesTree: Bayesian additive regression trees. comparison with previous approaches to causal inference from observational Estimating individual treatment effect: Generalization bounds and Wager, Stefan and Athey, Susan. Share on. This repo contains the neural network based counterfactual regression implementation for Ad attribution. PDF Learning Representations for Counterfactual Inference - arXiv We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. To rectify this problem, we use a nearest neighbour approximation ^NN-PEHE of the ^PEHE metric for the binary Shalit etal. Rubin, Donald B. Estimating causal effects of treatments in randomized and nonrandomized studies. Doubly robust policy evaluation and learning. (2017); Schuler etal. arXiv Vanity renders academic papers from For the python dependencies, see setup.py. Susan Athey, Julie Tibshirani, and Stefan Wager. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Morgan, Stephen L and Winship, Christopher. We performed experiments on several real-world and semi-synthetic datasets that showed that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. }Qm4;)v in Language Science and Technology from Saarland University and his A.B. Please download or close your previous search result export first before starting a new bulk export.
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