In. You can look at the slides here. You can also reproduce the figures in our manuscript by running the R-scripts in. Papers With Code is a free resource with all data licensed under. endobj To assess how the predictive performance of the different methods is influenced by increasing amounts of treatment assignment bias, we evaluated their performances on News-8 while varying the assignment bias coefficient on the range of 5 to 20 (Figure 5). This work was partially funded by the Swiss National Science Foundation (SNSF) project No. This indicates that PM is effective with any low-dimensional balancing score. counterfactual inference. Authors: Fredrik D. Johansson. Learning representations for counterfactual inference. Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". The propensity score with continuous treatments. Observational data, i.e. We consider the task of answering counterfactual questions such as, We are preparing your search results for download We will inform you here when the file is ready. The set of available treatments can contain two or more treatments. "7B}GgRvsp;"DD-NK}si5zU`"98}02 Jinsung Yoon, James Jordon, and Mihaela vander Schaar. This work contains the following contributions: We introduce Perfect Match (PM), a simple methodology based on minibatch matching for learning neural representations for counterfactual inference in settings with any number of treatments. Technical report, University of Illinois at Urbana-Champaign, 2008. PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. practical algorithm design. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Edit social preview. Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. Among States that did not Expand Medicaid, CETransformer: Casual Effect Estimation via Transformer Based Run the command line configurations from the previous step in a compute environment of your choice. Swaminathan, Adith and Joachims, Thorsten. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. [Takeuchi et al., 2021] Takeuchi, Koh, et al. 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). 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. We also found that matching on the propensity score was, in almost all cases, not significantly different from matching on X directly when X was low-dimensional, or a low-dimensional representation of X when X was high-dimensional (+ on X). Use of the logistic model in retrospective studies. (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. (2007), BART Chipman etal. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. 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. PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). (2011). To elucidate to what degree this is the case when using the matching-based methods we compared, we evaluated the respective training dynamics of PM, PSMPM and PSMMI (Figure 3). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. https://archive.ics.uci.edu/ml/datasets/bag+of+words. Identification and estimation of causal effects of multiple (2017).. Please try again. The script will print all the command line configurations (13000 in total) you need to run to obtain the experimental results to reproduce the IHDP results. However, it has been shown that hidden confounders may not necessarily decrease the performance of ITE estimators in practice if we observe suitable proxy variables Montgomery etal. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. We did so by using k head networks, one for each treatment over a set of shared base layers, each with L layers. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. (2016) that attempt to find such representations by minimising the discrepancy distance Mansour etal. While the underlying idea behind PM is simple and effective, it has, to the best of our knowledge, not yet been explored. We calculated the PEHE (Eq. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". << /Filter /FlateDecode /Length 529 >> We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. This repo contains the neural network based counterfactual regression implementation for Ad attribution. endobj Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. We extended the original dataset specification in Johansson etal. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. stream 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. questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". Learning Disentangled Representations for CounterFactual Regression Repeat for all evaluated percentages of matched samples. CSE, Chalmers University of Technology, Gteborg, Sweden . RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ (2009) between treatment groups, and Counterfactual Regression Networks (CFRNET) Shalit etal. Upon convergence, under assumption (1) and for. Accessed: 2016-01-30. $ @?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 Estimation and inference of heterogeneous treatment effects using Marginal structural models and causal inference in epidemiology. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. The distribution of samples may therefore differ significantly between the treated group and the overall population. Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. The coloured lines correspond to the mean value of the factual error (, Change in error (y-axes) in terms of precision in estimation of heterogenous effect (PEHE) and average treatment effect (ATE) when increasing the percentage of matches in each minibatch (x-axis). However, current methods for training neural networks for counterfactual . 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. Sign up to our mailing list for occasional updates. Dorie, Vincent. 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. M.Blondel, P.Prettenhofer, R.Weiss, V.Dubourg, J.Vanderplas, A.Passos, https://github.com/vdorie/npci, 2016. Domain adaptation for statistical classifiers. The outcomes were simulated using the NPCI package from Dorie (2016)222We used the same simulated outcomes as Shalit etal. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and [2023.04.12]: adding a more detailed sd-webui . BayesTree: Bayesian additive regression trees. One fundamental problem in the learning treatment effect from observational (2017). GANITE uses a complex architecture with many hyperparameters and sub-models that may be difficult to implement and optimise. D.Cournapeau, M.Brucher, M.Perrot, and E.Duchesnay. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Your search export query has expired. This is likely due to the shared base layers that enable them to efficiently share information across the per-treatment representations in the head networks. However, in many settings of interest, randomised experiments are too expensive or time-consuming to execute, or not possible for ethical reasons Carpenter (2014); Bothwell etal. Since the original TARNET was limited to the binary treatment setting, we extended the TARNET architecture to the multiple treatment setting (Figure 1). Perfect Match: A Simple Method for Learning Representations For Learning representations for counterfactual inference. The topic for this semester at the machine learning seminar was causal inference. On the binary News-2, PM outperformed all other methods in terms of PEHE and ATE. Matching as nonparametric preprocessing for reducing model dependence Inference on counterfactual distributions. Doubly robust estimation of causal effects. Chernozhukov, Victor, Fernndez-Val, Ivn, and Melly, Blaise. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 373 0 obj 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). Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks d909b/perfect_match ICLR 2019 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. For low-dimensional datasets, the covariates X are a good default choice as their use does not require a model of treatment propensity. His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. endobj All rights reserved. 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. dimensionality. F.Pedregosa, G.Varoquaux, A.Gramfort, V.Michel, B.Thirion, O.Grisel, Note that we lose the information about the precision in estimating ITE between specific pairs of treatments by averaging over all (k2) pairs. (2018) and multiple treatment settings for model selection. functions. How does the relative number of matched samples within a minibatch affect performance? Note: Create a results directory before executing Run.py. 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. We propose a new algorithmic framework for counterfactual GANITE: Estimation of Individualized Treatment Effects using Simulated data has been used as the input to PrepareData.py which would be followed by the execution of Run.py. In International Conference on Learning Representations. Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . Jingyu He, Saar Yalov, and P Richard Hahn. Create a folder to hold the experimental results. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. 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. treatments under the conditional independence assumption. 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). !lTv[ sj Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. Rosenbaum, Paul R and Rubin, Donald B. Susan Athey, Julie Tibshirani, and Stefan Wager. Counterfactual inference enables one to answer "What if. causal effects. (2007). Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. Counterfactual inference enables one to answer "What if?" The ATE measures the average difference in effect across the whole population (Appendix B). Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. ITE estimation from observational data is difficult for two reasons: Firstly, we never observe all potential outcomes. In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. The primary metric that we optimise for when training models to estimate ITE is the PEHE Hill (2011). Generative Adversarial Nets. (2017). Domain adaptation: Learning bounds and algorithms. 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. x4k6Q0z7F56K.HtB$w}s{y_5\{_{? 1 Paper We refer to the special case of two available treatments as the binary treatment setting. 2023 Neural Causal Models for Counterfactual Identification and Estimation Xia, K., Pan, Y., and Bareinboim, E. (ICLR-23) In Proceedings of the 11th Eleventh International Conference on Learning Representations, Feb 2023 [ pdf , arXiv ] 2022 Causal Transportability for Visual Recognition xcbdg`b`8 $S&`6Ah :H) @DH301?e`%x]0 > ; In The 22nd International Conference on Artificial Intelligence and Statistics. [width=0.25]img/mse Rubin, Donald B. Causal inference using potential outcomes. (2017) (Appendix H) to the multiple treatment setting. data is confounder identification and balancing. Mansour, Yishay, Mohri, Mehryar, and Rostamizadeh, Afshin. In this paper, we propose Counterfactual Explainable Recommendation ( Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. 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. This setup comes up in diverse areas, for example off-policy evalu-ation in reinforcement learning (Sutton & Barto,1998), Langford, John, Li, Lihong, and Dudk, Miroslav. 0 qA0)#@K5Ih-X8oYH>2{wB2(k`:0P}U)j|B5z.O{?T ;?eKS+9S!9GQAMTl/! We therefore conclude that matching on the propensity score or a low-dimensional representation of X and using the TARNET architecture are sensible default configurations, particularly when X is high-dimensional. The ACM Digital Library is published by the Association for Computing Machinery. 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. Limits of estimating heterogeneous treatment effects: Guidelines for ^mPEHE In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Quick introduction to CounterFactual Regression (CFR) Our experiments demonstrate that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmarks, particularly in settings with many treatments. Propensity Dropout (PD) Alaa etal. We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. In medicine, for example, we would be interested in using data of people that have been treated in the past to predict what medications would lead to better outcomes for new patients Shalit etal. Article . Counterfactual Inference | Papers With Code In endstream We outline the Perfect Match (PM) algorithm in Algorithm 1 (complexity analysis and implementation details in Appendix D). 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. We develop performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual treatment effects in the setting with multiple available treatments. (2016). We therefore suggest to run the commands in parallel using, e.g., a compute cluster. Papers With Code is a free resource with all data licensed under. We also evaluated preprocessing the entire training set with PSM using the same matching routine as PM (PSMPM) and the "MatchIt" package (PSMMI, Ho etal. Share on (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Want to hear about new tools we're making? ;'/ Learning representations for counterfactual inference. The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. 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). i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR 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. $ ?>jYJW*9Y!WLPD vu{B" j!P?D ; =?5DEE@?8 7@io$. 368 0 obj Add a If you find a rendering bug, file an issue on GitHub. Jennifer L Hill. Examples of representation-balancing methods are Balancing Neural Networks Johansson etal. task. Candidate at the Saarland University Graduate School of Computer Science, where he is advised by Dietrich Klakow. accumulation of data in fields such as healthcare, education, employment and Date: February 12, 2020. Repeat for all evaluated methods / levels of kappa combinations. bartMachine: Machine learning with Bayesian additive regression By modeling the different causal relations among observed pre-treatment variables, treatment and outcome, we propose a synergistic learning framework to 1) identify confounders by learning decomposed representations of both confounders and non-confounders, 2) balance confounder with sample re-weighting technique, and simultaneously 3) estimate the treatment effect in observational studies via counterfactual inference. We found that NN-PEHE correlates significantly better with the PEHE than MSE (Figure 2). Our deep learning algorithm significantly outperforms the previous state-of-the-art. Recursive partitioning for personalization using observational data. 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. 3) for News-4/8/16 datasets. Our empirical results demonstrate that the proposed =1(k2)k1i=0i1j=0^PEHE,i,j A comparison of methods for model selection when estimating Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. << /Linearized 1 /L 849041 /H [ 2447 819 ] /O 371 /E 54237 /N 78 /T 846567 >> << /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 >> Estimating categorical counterfactuals via deep twin networks causes of both the treatment and the outcome, some variables only contribute to The samples X represent news items consisting of word counts xiN, the outcome yjR is the readers opinion of the news item, and the k available treatments represent various devices that could be used for viewing, e.g. A simple method for estimating interactions between a treatment and a large number of covariates. The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. by learning decomposed representation of confounders and non-confounders, and Kang, Joseph DY and Schafer, Joseph L. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. The chosen architecture plays a key role in the performance of neural networks when attempting to learn representations for counterfactual inference Shalit etal. inference. 36 0 obj << endobj Once you have completed the experiments, you can calculate the summary statistics (mean +- standard deviation) over all the repeated runs using the. %PDF-1.5 (2) in Linguistics and Computation from Princeton University. These k-Nearest-Neighbour (kNN) methods Ho etal. Representation Learning. The source code for this work is available at https://github.com/d909b/perfect_match. 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. Matching methods are among the conceptually simplest approaches to estimating ITEs. Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah. Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. PMLR, 2016. medication?". By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output.
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