Papers With Code is a free resource with all data licensed under. A tag already exists with the provided branch name. \includegraphics[width=0.25]img/nn_pehe. Linear regression models can either be used for building one model, with the treatment as an input feature, or multiple separate models, one for each treatment Kallus (2017). Brookhart, and Marie Davidian. Home Browse by Title Proceedings ICML'16 Learning representations for counterfactual inference. Secondly, the assignment of cases to treatments is typically biased such that cases for which a given treatment is more effective are more likely to have received that treatment. available at this link. 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 The central role of the propensity score in observational studies for data that has not been collected in a randomised experiment, on the other hand, is often readily available in large quantities. As computing systems are more frequently and more actively intervening to improve people's work and daily lives, it is critical to correctly predict and understand the causal effects of these interventions. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. 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. Natural language is the extreme case of complex-structured data: one thousand mathematical dimensions still cannot capture all of the kinds of information encoded by a word in its context. Note: Create a results directory before executing Run.py. As training data, we receive samples X and their observed factual outcomes yj when applying one treatment tj, the other outcomes can not be observed. % (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. This work was partially funded by the Swiss National Science Foundation (SNSF) project No. Analogously to Equations (2) and (3), the ^NN-PEHE metric can be extended to the multiple treatment setting by considering the mean ^NN-PEHE between all (k2) possible pairs of treatments (Appendix F). Representation Learning. Counterfactual reasoning and learning systems: The example of computational advertising. Come up with a framework to train models for factual and counterfactual inference. You can register new benchmarks for use from the command line by adding a new entry to the, After downloading IHDP-1000.tar.gz, you must extract the files into the. Bang, Heejung and Robins, James M. Doubly robust estimation in missing data and causal inference models. DanielE Ho, Kosuke Imai, Gary King, and ElizabethA Stuart. 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). The advantage of matching on the minibatch level, rather than the dataset level Ho etal. (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. Limits of estimating heterogeneous treatment effects: Guidelines for 167302 within the National Research Program (NRP) 75 "Big Data". CSE, Chalmers University of Technology, Gteborg, Sweden. In medicine, for example, treatment effects are typically estimated via rigorous prospective studies, such as randomised controlled trials (RCTs), and their results are used to regulate the approval of treatments. M.Blondel, P.Prettenhofer, R.Weiss, V.Dubourg, J.Vanderplas, A.Passos, Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks, Correlation MSE and NN-PEHE with PEHE (Figure 3), https://cran.r-project.org/web/packages/latex2exp/vignettes/using-latex2exp.html, The available command line parameters for runnable scripts are described in, You can add new baseline methods to the evaluation by subclassing, You can register new methods for use from the command line by adding a new entry to the. Jonas Peters, Dominik Janzing, and Bernhard Schlkopf. Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. We then defined the unscaled potential outcomes yj=~yj[D(z(X),zj)+D(z(X),zc)] as the ideal potential outcomes ~yj weighted by the sum of distances to centroids zj and the control centroid zc using the Euclidean distance as distance D. We assigned the observed treatment t using t|xBern(softmax(yj)) with a treatment assignment bias coefficient , and the true potential outcome yj=Cyj as the unscaled potential outcomes yj scaled by a coefficient C=50. Accessed: 2016-01-30. PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. Identification and estimation of causal effects of multiple (2016) to enable the simulation of arbitrary numbers of viewing devices. MarkR Montgomery, Michele Gragnolati, KathleenA Burke, and Edmundo Paredes. BayesTree: Bayesian additive regression trees. This repository contains the source code used to evaluate PM and most of the existing state-of-the-art methods at the time of publication of our manuscript. Run the following scripts to obtain mse.txt, pehe.txt and nn_pehe.txt for use with the. Dorie, Vincent. (2011). AhmedM Alaa, Michael Weisz, and Mihaela vander Schaar. (2017) (Appendix H) to the multiple treatment setting. DanielE Ho, Kosuke Imai, Gary King, ElizabethA Stuart, etal. Daume III, Hal and Marcu, Daniel. 369 0 obj Newman, David. Counterfactual inference enables one to answer "What if?" By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output. %PDF-1.5 In these situations, methods for estimating causal effects from observational data are of paramount importance. LauraE. Bothwell, JeremyA. Greene, ScottH. Podolsky, and DavidS. Jones. Invited commentary: understanding bias amplification. (2010); Chipman and McCulloch (2016), Random Forests (RF) Breiman (2001), CF Wager and Athey (2017), GANITE Yoon etal. Papers With Code is a free resource with all data licensed under. In particular, the source code is designed to be easily extensible with (1) new methods and (2) new benchmark datasets. If a patient is given a treatment to treat her symptoms, we never observe what would have happened if the patient was prescribed a potential alternative treatment in the same situation. method can precisely identify and balance confounders, while the estimation of You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. We therefore suggest to run the commands in parallel using, e.g., a compute cluster. For high-dimensional datasets, the scalar propensity score is preferable because it avoids the curse of dimensionality that would be associated with matching on the potentially high-dimensional X directly. Bayesian inference of individualized treatment effects using We develop performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual treatment effects in the setting with multiple available treatments. Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Sign up to our mailing list for occasional updates. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. We perform experiments that demonstrate that PM is robust to a high level of treatment assignment bias and outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmark datasets. The conditional probability p(t|X=x) of a given sample x receiving a specific treatment t, also known as the propensity score Rosenbaum and Rubin (1983), and the covariates X themselves are prominent examples of balancing scores Rosenbaum and Rubin (1983); Ho etal. To run BART, you need to have the R-packages, To run Causal Forests, you need to have the R-package, To reproduce the paper's figures, you need to have the R-package. Learning representations for counterfactual inference. van der Laan, Mark J and Petersen, Maya L. Causal effect models for realistic individualized treatment and intention to treat rules. Improving Unsupervised Vector-Space Thematic Fit Evaluation via Role-Filler Prototype Clustering, Sub-Word Similarity-based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modeling. Doubly robust policy evaluation and learning. In addition, we extended the TARNET architecture and the PEHE metric to settings with more than two treatments, and introduced a nearest neighbour approximation of PEHE and mPEHE that can be used for model selection without having access to counterfactual outcomes. The outcomes were simulated using the NPCI package from Dorie (2016)222We used the same simulated outcomes as Shalit etal. Shalit etal. Learning representations for counterfactual inference - ICML, 2016. Bag of words data set. To model that consumers prefer to read certain media items on specific viewing devices, we train a topic model on the whole NY Times corpus and define z(X) as the topic distribution of news item X. [width=0.25]img/mse Your file of search results citations is now ready. See https://www.r-project.org/ for installation instructions. MicheleJonsson Funk, Daniel Westreich, Chris Wiesen, Til Strmer, M.Alan Estimation, Treatment Effect Estimation with Unmeasured Confounders in Data Fusion, Learning Disentangled Representations for Counterfactual Regression via His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. The root problem is that we do not have direct access to the true error in estimating counterfactual outcomes, only the error in estimating the observed factual outcomes. non-confounders would generate additional bias for treatment effect estimation. << /Filter /FlateDecode /Length 529 >> A First Supervised Approach Given n samples fx i;t i;yF i g n i=1, where y F i = t iY 1(x i)+(1 t i)Y 0(x i) Learn . Learning Representations for Counterfactual Inference choice without knowing what would be the feedback for other possible choices. $ @?g7F1Q./bA!/g[Ee TEOvuJDF QDzF5O2TP?5+7WW]zBVR!vBZ/j#F y2"o|4ll{b33p>i6MwE/q {B#uXzZM;bXb(:#aJCeocD?gb]B<7%{jb0r ;oZ1KZ(OZ2[)k0"1S]^L4Yh-gp g|XK`$QCj 30G{$mt We outline the Perfect Match (PM) algorithm in Algorithm 1 (complexity analysis and implementation details in Appendix D). Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The chosen architecture plays a key role in the performance of neural networks when attempting to learn representations for counterfactual inference Shalit etal. We evaluated the counterfactual inference performance of the listed models in settings with two or more available treatments (Table 1, ATEs in Appendix Table S3). 1) and ATE (Appendix B) for the binary IHDP and News-2 datasets, and the ^mPEHE (Eq. Jennifer L Hill. << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> Morgan, Stephen L and Winship, Christopher. In addition, we assume smoothness, i.e. In International Conference on Learning Representations. Repeat for all evaluated methods / levels of kappa combinations. Share on. Repeat for all evaluated percentages of matched samples. Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah. This work was partially funded by the Swiss National Science Foundation (SNSF) project No. Make sure you have all the requirements listed above. We did so by using k head networks, one for each treatment over a set of shared base layers, each with L layers. The script will print all the command line configurations (450 in total) you need to run to obtain the experimental results to reproduce the News results. data is confounder identification and balancing. The propensity score with continuous treatments. NPCI: Non-parametrics for causal inference. (2017). Most of the previous methods Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. (2016) that attempt to find such representations by minimising the discrepancy distance Mansour etal. In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. Counterfactual inference enables one to answer "What if?" Evaluating the econometric evaluations of training programs with r/WI7FW*^e~gNdk}4]iE3it0W}]%Cw5"$HhKxYlR&{Y_{R~MkE}R0#~8$LVDt*EG_Q hMZk5jCNm1Y%i8vb3 E8&R/g2}h%X7.jR*yqmEi|[$/?XBo{{kSjWIlW medication?". RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ [HJ)mD:K`G?/BPWw(a&ggl }[OvP ps@]TZP?x ;_[YN^0'5 Domain adaptation for statistical classifiers. On the News-4/8/16 datasets with more than two treatments, PM consistently outperformed all other methods - in some cases by a large margin - on both metrics with the exception of the News-4 dataset, where PM came second to PD. Empirical results on synthetic and real-world datasets demonstrate that the proposed method can precisely decompose confounders and achieve a more precise estimation of treatment effect than baselines. (2016). "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference." arXiv preprint arXiv:2102.03980, 2021. Treatment effect estimation with disentangled latent factors, Adversarial De-confounding in Individualised Treatment Effects We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". Author(s): Patrick Schwab, ETH Zurich patrick.schwab@hest.ethz.ch, Lorenz Linhardt, ETH Zurich llorenz@student.ethz.ch and Walter Karlen, ETH Zurich walter.karlen@hest.ethz.ch. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. (2017) is another method using balancing scores that has been proposed to dynamically adjust the dropout regularisation strength for each observed sample depending on its treatment propensity. Upon convergence at the training data, neural networks trained using virtually randomised minibatches in the limit N remove any treatment assignment bias present in the data. the treatment effect performs better than the state-of-the-art methods on both 167302 within the National Research Program (NRP) 75 Big Data. counterfactual inference. Robins, James M, Hernan, Miguel Angel, and Brumback, Babette. Technical report, University of Illinois at Urbana-Champaign, 2008. We focus on counterfactual questions raised by what areknown asobservational studies. (2018), Balancing Neural Network (BNN) Johansson etal. PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. @E)\a6Hk$$x9B]aV`'iuD Please try again. (2017). PM effectively controls for biased assignment of treatments in observational data by augmenting every sample within a minibatch with its closest matches by propensity score from the other treatments. (2016). (2018) and multiple treatment settings for model selection. endstream Our empirical results demonstrate that the proposed learning. For the python dependencies, see setup.py. multi-task gaussian processes. 2#w2;0USFJFxp G+=EtA65ztTu=i7}qMX`]vhfw7uD/k^[%_ .r d9mR5GMEe^; :$LZ9&|cvrDTD]Dn@9DZO8=VZe+IjBX{\q Ep8[Cw.M'ZK4b>.R7,&z>@|/:\4w&"sMHNcj7z3GrT |WJ-P4;nn[\wEIwF'E8"Q/JVAj8*k$:l2NsAi:NvmzSKO4gMg?#bYE65lf pAy6s9>->0| >b8%7a/ KqG9cw|w]jIDic. i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR (2011) to estimate p(t|X) for PM on the training set. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. A supervised model navely trained to minimise the factual error would overfit to the properties of the treated group, and thus not generalise well to the entire population. Mansour, Yishay, Mohri, Mehryar, and Rostamizadeh, Afshin. Your results should match those found in the. The optimisation of CMGPs involves a matrix inversion of O(n3) complexity that limits their scalability. Matching methods are among the conceptually simplest approaches to estimating ITEs. << /Type /XRef /Length 73 /Filter /FlateDecode /DecodeParms << /Columns 4 /Predictor 12 >> /W [ 1 2 1 ] /Index [ 367 184 ] /Info 183 0 R /Root 369 0 R /Size 551 /Prev 846568 /ID [<6128b543239fbdadfc73903b5348344b>] >> Generative Adversarial Nets for inference of Individualised Treatment Effects (GANITE) Yoon etal. algorithms. {6&m=>9wB$ (2017); Schuler etal. endobj The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Causal inference using potential outcomes: Design, modeling, (2009) between treatment groups, and Counterfactual Regression Networks (CFRNET) Shalit etal. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. We performed experiments on two real-world and semi-synthetic datasets with binary and multiple treatments in order to gain a better understanding of the empirical properties of PM. Given the training data with factual outcomes, we wish to train a predictive model ^f that is able to estimate the entire potential outcomes vector ^Y with k entries ^yj. Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . Symbols correspond to the mean value of, Comparison of several state-of-the-art methods for counterfactual inference on the test set of the News-8 dataset when varying the treatment assignment imbalance, Comparison of methods for counterfactual inference with two and more available treatments on IHDP and News-2/4/8/16. We use cookies to ensure that we give you the best experience on our website. The strong performance of PM across a wide range of datasets with varying amounts of treatments is remarkable considering how simple it is compared to other, highly specialised methods. Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. smartphone, tablet, desktop, television or others Johansson etal. 2C&( ??;9xCc@e%yeym? Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. Tree-based methods train many weak learners to build expressive ensemble models. Scatterplots show a subsample of 1400 data points. To judge whether NN-PEHE is more suitable for model selection for counterfactual inference than MSE, we compared their respective correlations with the PEHE on IHDP. This setup comes up in diverse areas, for example off-policy evalu-ation in reinforcement learning (Sutton & Barto,1998), << /Names 366 0 R /OpenAction 483 0 R /Outlines 470 0 R /PageLabels << /Nums [ 0 << /P (0) >> 1 << /P (1) >> 4 << /P (2) >> 5 << /P (3) >> 6 << /P (4) >> 7 << /P (5) >> 11 << /P (6) >> 14 << /P (7) >> 16 << /P (8) >> 20 << /P (9) >> 25 << /P (10) >> 30 << /P (11) >> 32 << /P (12) >> 34 << /P (13) >> 35 << /P (14) >> 39 << /P (15) >> 40 << /P (16) >> 44 << /P (17) >> 49 << /P (18) >> 50 << /P (19) >> 54 << /P (20) >> 57 << /P (21) >> 61 << /P (22) >> 64 << /P (23) >> 65 << /P (24) >> 69 << /P (25) >> 70 << /P (26) >> 77 << /P (27) >> ] >> /PageMode /UseOutlines /Pages 469 0 R /Type /Catalog >> This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. treatments under the conditional independence assumption. However, one can inspect the pair-wise PEHE to obtain the whole picture. (2017); Alaa and Schaar (2018). Free Access. %PDF-1.5 =1(k2)k1i=0i1j=0^PEHE,i,j (2018) address ITE estimation using counterfactual and ITE generators. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Rubin, Donald B. Causal inference using potential outcomes. Representation learning: A review and new perspectives. (3). Recent Research PublicationsImproving Unsupervised Vector-Space Thematic Fit Evaluation via Role-Filler Prototype ClusteringSub-Word Similarity-based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modeling, Copyright Regents of the University of California. Correlation analysis of the real PEHE (y-axis) with the mean squared error (MSE; left) and the nearest neighbour approximation of the precision in estimation of heterogenous effect (NN-PEHE; right) across over 20000 model evaluations on the validation set of IHDP. "7B}GgRvsp;"DD-NK}si5zU`"98}02 The variational fair auto encoder. However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both. Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. After the experiments have concluded, use. You can look at the slides here. Hill, Jennifer L. Bayesian nonparametric modeling for causal inference. We consider the task of answering counterfactual questions such as, }Qm4;)v In general, not all the observed pre-treatment variables are confounders that refer to the common causes of the treatment and the outcome, some variables only contribute to the treatment and some only contribute to the outcome. Learning disentangled representations for counterfactual regression. We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. However, they are predominantly focused on the most basic setting with exactly two available treatments. Does model selection by NN-PEHE outperform selection by factual MSE? GANITE uses a complex architecture with many hyperparameters and sub-models that may be difficult to implement and optimise. (2011). (2017) subsequently introduced the TARNET architecture to rectify this issue. You can add new benchmarks by implementing the benchmark interface, see e.g. Observational data, i.e. Note that we only evaluate PM, + on X, + MLP, PSM on Jobs. All rights reserved. Please download or close your previous search result export first before starting a new bulk export. Austin, Peter C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. 371 0 obj zz !~A|66}$EPp("i n $* causes of both the treatment and the outcome, some variables only contribute to Want to hear about new tools we're making? We consider fully differentiable neural network models ^f optimised via minibatch stochastic gradient descent (SGD) to predict potential outcomes ^Y for a given sample x. Login. https://cran.r-project.org/package=BayesTree/, 2016. The script will print all the command line configurations (180 in total) you need to run to obtain the experimental results to reproduce the TCGA results. We calculated the PEHE (Eq. To run the TCGA and News benchmarks, you need to download the SQLite databases containing the raw data samples for these benchmarks (news.db and tcga.db). Recursive partitioning for personalization using observational data. This is sometimes referred to as bandit feedback (Beygelzimer et al.,2010). /Length 3974 Or, have a go at fixing it yourself the renderer is open source! endobj https://dl.acm.org/doi/abs/10.5555/3045390.3045708. We perform extensive experiments on semi-synthetic, real-world data in settings with two and more treatments. Upon convergence, under assumption (1) and for. Children that did not receive specialist visits were part of a control group. Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates << /Annots [ 484 0 R ] /Contents 372 0 R /MediaBox [ 0 0 362.835 272.126 ] /Parent 388 0 R /Resources 485 0 R /Trans << /S /R >> /Type /Page >> We propose a new algorithmic framework for counterfactual As outlined previously, if we were successful in balancing the covariates using the balancing score, we would expect that the counterfactual error is implicitly and consistently improved alongside the factual error. questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. task. The IHDP dataset Hill (2011) contains data from a randomised study on the impact of specialist visits on the cognitive development of children, and consists of 747 children with 25 covariates describing properties of the children and their mothers. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. On IHDP, the PM variants reached the best performance in terms of PEHE, and the second best ATE after CFRNET. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. He received his M.Sc. Jinsung Yoon, James Jordon, and Mihaela vander Schaar. Learning Disentangled Representations for CounterFactual Regression Negar Hassanpour, Russell Greiner 25 Sep 2019, 12:15 (modified: 11 Mar 2020, 00:33) ICLR 2020 Conference Blind Submission Readers: Everyone Keywords: Counterfactual Regression, Causal Effect Estimation, Selection Bias, Off-policy Learning Finally, although TARNETs trained with PM have similar asymptotic properties as kNN, we found that TARNETs trained with PM significantly outperformed kNN in all cases. >> Are you sure you want to create this branch? 367 0 obj Causal Multi-task Gaussian Processes (CMGP) Alaa and vander Schaar (2017) apply a multi-task Gaussian Process to ITE estimation. 368 0 obj << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> On causal and anticausal learning. Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k All other results are taken from the respective original authors' manuscripts. stream To address these problems, we introduce Perfect Match (PM), a simple method for training neural networks for counterfactual inference that extends to any number of treatments. realized confounder balancing by treating all observed variables as xTn0+H6:iUNAMlm-*P@3,K)WL PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. Wager, Stefan and Athey, Susan. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Examples of representation-balancing methods are Balancing Neural Networks Johansson etal. =0 indicates no assignment bias. The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. Observational studies are rising in importance due to the widespread Candidate, Saarland UniversityDate:Monday, May 8, 2017Time: 11amLocation: Room 1202, CSE BuildingHost: CSE Prof. Mohan Paturi (paturi@eng.ucsd.edu)Representation Learning: What Is It and How Do You Teach It?Abstract:In this age of Deep Learning, Big Data, and ubiquitous graphics processors, the knowledge frontier is often controlled not by computing power, but by the usefulness of how scientists choose to represent their data.
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