learning representations for counterfactual inference github

A tag already exists with the provided branch name. Learning representations for counterfactual inference. In. propose a synergistic learning framework to 1) identify and balance confounders Recursive partitioning for personalization using observational data. 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. Bayesian inference of individualized treatment effects using One fundamental problem in the learning treatment effect from observational (2018), Balancing Neural Network (BNN) Johansson etal. Domain adaptation for statistical classifiers. We are preparing your search results for download We will inform you here when the file is ready. dimensionality. The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. (2011), is that it reduces the variance during training which in turn leads to better expected performance for counterfactual inference (Appendix E). On the binary News-2, PM outperformed all other methods in terms of PEHE and ATE. Our deep learning algorithm significantly outperforms the previous Note the installation of rpy2 will fail if you do not have a working R installation on your system (see above). Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. To address the treatment assignment bias inherent in observational data, we propose to perform SGD in a space that approximates that of a randomised experiment using the concept of balancing scores. Once you have completed the experiments, you can calculate the summary statistics (mean +- standard deviation) over all the repeated runs using the. 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. ,E^-"4nhi/dX]/hs9@A$}M\#6soa0YsR/X#+k!"uqAJ3un>e-I~8@f*M9:3qc'RzH ,` https://archive.ics.uci.edu/ml/datasets/bag+of+words. Accessed: 2016-01-30. Shalit etal. Measuring living standards with proxy variables. (2007). accumulation of data in fields such as healthcare, education, employment and in Linguistics and Computation from Princeton University. The IHDP dataset is biased because the treatment groups had a biased subset of the treated population removed Shalit etal. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. << /Type /XRef /Length 73 /Filter /FlateDecode /DecodeParms << /Columns 4 /Predictor 12 >> /W [ 1 2 1 ] /Index [ 367 184 ] /Info 183 0 R /Root 369 0 R /Size 551 /Prev 846568 /ID [<6128b543239fbdadfc73903b5348344b>] >> Your file of search results citations is now ready. rk*>&TaYh%gc,(| DiJIRR?ZzfT"Zv$]}-P+"{Z4zVSNXs$kHyS$z>q*BHA"6#d.wtt3@V^SL+xm=,mh2\'UHum8Nb5gI >VtU i-zkAz~b6;]OB9:>g#{(XYW>idhKt task. 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. 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. A general limitation of this work, and most related approaches, to counterfactual inference from observational data is that its underlying theory only holds under the assumption that there are no unobserved confounders - which guarantees identifiability of the causal effects. We use cookies to ensure that we give you the best experience on our website. (2017). 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. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. xcbdg`b`8 $S&`6Ah :H) @DH301?e`%x]0 > ; bartMachine: Machine learning with Bayesian additive regression However, current methods for training neural networks for counterfactual . The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. Papers With Code is a free resource with all data licensed under. 2C&( ??;9xCc@e%yeym? You signed in with another tab or window. PMLR, 1130--1138. Edit social preview. Batch learning from logged bandit feedback through counterfactual risk minimization. Federated unsupervised representation learning, FITEE, 2022. We outline the Perfect Match (PM) algorithm in Algorithm 1 (complexity analysis and implementation details in Appendix D). (2011). 373 0 obj random forests. simultaneously 2) estimate the treatment effect in observational studies via Learning Representations for Counterfactual Inference | DeepAI In this sense, PM can be seen as a minibatch sampling strategy Csiba and Richtrik (2018) designed to improve learning for counterfactual inference. CRM, also known as batch learning from bandit feedback, optimizes the policy model by maximizing its reward estimated with a counterfactual risk estimator (Dudk, Langford, and Li 2011 . 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. cq?g GANITE: Estimation of Individualized Treatment Effects using To perform counterfactual inference, we require knowledge of the underlying. The ATE measures the average difference in effect across the whole population (Appendix B). 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. This regularises the treatment assignment bias but also introduces data sparsity as not all available samples are leveraged equally for training. Observational studies are rising in importance due to the widespread To run the IHDP benchmark, you need to download the raw IHDP data folds as used by Johanson et al. Make sure you have all the requirements listed above. Rosenbaum, Paul R and Rubin, Donald B. CauseBox | Proceedings of the 30th ACM International Conference on ]|2jZ;lU.t`' 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. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. 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. https://github.com/vdorie/npci, 2016. Propensity Dropout (PD) Alaa etal. 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. We focus on counterfactual questions raised by what areknown asobservational studies. endobj Identification and estimation of causal effects of multiple Learning representations for counterfactual inference. While the underlying idea behind PM is simple and effective, it has, to the best of our knowledge, not yet been explored. This indicates that PM is effective with any low-dimensional balancing score. Quick introduction to CounterFactual Regression (CFR) (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). 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. (2010); Chipman and McCulloch (2016), Random Forests (RF) Breiman (2001), CF Wager and Athey (2017), GANITE Yoon etal. Matching methods estimate the counterfactual outcome of a sample X with respect to treatment t using the factual outcomes of its nearest neighbours that received t, with respect to a metric space. Accessed: 2016-01-30. 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. A Simple Method for Learning Representations For Counterfactual 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. Counterfactual inference from observational data always requires further assumptions about the data-generating process Pearl (2009); Peters etal. MicheleJonsson Funk, Daniel Westreich, Chris Wiesen, Til Strmer, M.Alan Newman, David. Fredrik Johansson, Uri Shalit, and David Sontag. Article . Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. (2017) (Appendix H) to the multiple treatment setting. 1 Paper In TARNET, the jth head network is only trained on samples from treatment tj. As a secondary metric, we consider the error ATE in estimating the average treatment effect (ATE) Hill (2011). 2011. Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. Causal effect inference with deep latent-variable models. in Linguistics and Computation from Princeton University. CSE, Chalmers University of Technology, Gteborg, Sweden. << /Filter /FlateDecode /Length 529 >> 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. [Takeuchi et al., 2021] Takeuchi, Koh, et al. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. trees. Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates By modeling the different relations among variables, treatment and outcome, we Learning Representations for Counterfactual Inference d909b/perfect_match - Github To rectify this problem, we use a nearest neighbour approximation ^NN-PEHE of the ^PEHE metric for the binary Shalit etal. [HJ)mD:K`G?/BPWw(a&ggl }[OvP ps@]TZP?x ;_[YN^0'5 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. Learning representations for counterfactual inference | Proceedings of This makes it difficult to perform parameter and hyperparameter optimisation, as we are not able to evaluate which models are better than others for counterfactual inference on a given dataset. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. F.Pedregosa, G.Varoquaux, A.Gramfort, V.Michel, B.Thirion, O.Grisel, We repeated experiments on IHDP and News 1000 and 50 times, respectively. Representation learning: A review and new perspectives. The set of available treatments can contain two or more treatments. Generative Adversarial Nets for inference of Individualised Treatment Effects (GANITE) Yoon etal. inference. (2017); Alaa and Schaar (2018). Counterfactual inference enables one to answer "What if?" 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. Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. 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). multi-task gaussian processes. decisions. 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. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. We assigned a random Gaussian outcome distribution with mean jN(0.45,0.15) and standard deviation jN(0.1,0.05) to each centroid. }Qm4;)v Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. 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. Estimation, Treatment Effect Estimation with Unmeasured Confounders in Data Fusion, Learning Disentangled Representations for Counterfactual Regression via an exact match in the balancing score, for observed factual outcomes. Learning fair representations. We extended the original dataset specification in Johansson etal. Share on. You can also reproduce the figures in our manuscript by running the R-scripts in. How does the relative number of matched samples within a minibatch affect performance? (2017). Matching as nonparametric preprocessing for reducing model dependence the treatment and some contribute to the outcome. ecology. Perfect Match: A Simple Method for Learning Representations For Representation Learning: What Is It and How Do You Teach It? However, one can inspect the pair-wise PEHE to obtain the whole picture. individual treatment effects. We evaluated PM, ablations, baselines, and all relevant state-of-the-art methods: kNN Ho etal. endobj Prentice, Ross. (3). Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". BART: Bayesian additive regression trees. His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. 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 . In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Note that we only evaluate PM, + on X, + MLP, PSM on Jobs. stream 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 causal effects. Upon convergence, under assumption (1) and for N, a neural network ^f trained according to the PM algorithm is a consistent estimator of the true potential outcomes Y for each t. The optimal choice of balancing score for use in the PM algorithm depends on the properties of the dataset. Learning Decomposed Representation for Counterfactual Inference Finally, we show that learning rep-resentations that encourage similarity (also called balance)between the treatment and control populations leads to bet-ter counterfactual inference; this is in contrast to manymethods which attempt to create balance by re-weightingsamples (e.g., Bang & Robins, 2005; Dudk et al., 2011;Austin, 2011; Swaminathan Please try again. 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. Kang, Joseph DY and Schafer, Joseph L. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Date: February 12, 2020. Share on Are you sure you want to create this branch? Matching methods are among the conceptually simplest approaches to estimating ITEs. The role of the propensity score in estimating dose-response Since we performed one of the most comprehensive evaluations to date with four different datasets with varying characteristics, this repository may serve as a benchmark suite for developing your own methods for estimating causal effects using machine learning methods. in parametric causal inference. If you find a rendering bug, file an issue on GitHub. endobj Inference on counterfactual distributions. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. @E)\a6Hk$$x9B]aV`'iuD The propensity score with continuous treatments. << /Linearized 1 /L 849041 /H [ 2447 819 ] /O 371 /E 54237 /N 78 /T 846567 >> Note that we lose the information about the precision in estimating ITE between specific pairs of treatments by averaging over all (k2) pairs. Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Authors: Fredrik D. Johansson. In We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. (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. Please download or close your previous search result export first before starting a new bulk export. BayesTree: Bayesian additive regression trees. /Filter /FlateDecode experimental data. ;'/ We can neither calculate PEHE nor ATE without knowing the outcome generating process. Speaker: Clayton Greenberg, Ph.D. (2007), BART Chipman etal. In. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". stream Approximate nearest neighbors: towards removing the curse of We propose a new algorithmic framework for counterfactual A tag already exists with the provided branch name. << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> Gretton, Arthur, Borgwardt, Karsten M., Rasch, Malte J., Schlkopf, Bernhard, and Smola, Alexander. 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. For low-dimensional datasets, the covariates X are a good default choice as their use does not require a model of treatment propensity. In these situations, methods for estimating causal effects from observational data are of paramount importance. Inferring the causal effects of interventions is a central pursuit in many important domains, such as healthcare, economics, and public policy. Add a Examples of representation-balancing methods are Balancing Neural Networks Johansson etal. %PDF-1.5 % 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. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. Candidate at the Saarland University Graduate School of Computer Science, where he is advised by Dietrich Klakow. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. 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. !lTv[ sj For the python dependencies, see setup.py. MatchIt: nonparametric preprocessing for parametric causal You can use pip install . Propensity Score Matching (PSM) Rosenbaum and Rubin (1983) addresses this issue by matching on the scalar probability p(t|X) of t given the covariates X. $ @?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 treatments under the conditional independence assumption. See https://www.r-project.org/ for installation instructions. GitHub - ankits0207/Learning-representations-for-counterfactual %PDF-1.5 If you reference or use our methodology, code or results in your work, please consider citing: This project was designed for use with Python 2.7. 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). 368 0 obj Sign up to our mailing list for occasional updates. 167302 within the National Research Program (NRP) 75 "Big Data". Uri Shalit, FredrikD Johansson, and David Sontag. Technical report, University of Illinois at Urbana-Champaign, 2008. You signed in with another tab or window. Note: Create a results directory before executing Run.py. (2016). ci0pf=[3@Cm*A,rY`@n 9u_\p=p'h3C'[|kvZMJ:S=9dGC-!43BA RQqr01o:xG ?7>[pM)kC2@p%Np PMLR, 2016. 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. We found that including more matches indeed consistently reduces the counterfactual error up to 100% of samples matched. Methods that combine a model of the outcomes and a model of the treatment propensity in a manner that is robust to misspecification of either are referred to as doubly robust Funk etal. Balancing those A comparison of methods for model selection when estimating ITE estimation from observational data is difficult for two reasons: Firstly, we never observe all potential outcomes. (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. Gani, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, Franois, Marchand, Mario, and Lempitsky, Victor. Then, I will share the educational objectives for students of data science inspired by my research, and how, with interactive and innovative teaching, I have trained and will continue to train students to be successful in their scientific pursuits. The original experiments reported in our paper were run on Intel CPUs. Bigger and faster computation creates such an opportunity to answer what previously seemed to be unanswerable research questions, but also can be rendered meaningless if the structure of the data is not sufficiently understood. In, Strehl, Alex, Langford, John, Li, Lihong, and Kakade, Sham M. Learning from logged implicit exploration data. Among States that did not Expand Medicaid, CETransformer: Casual Effect Estimation via Transformer Based "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference." arXiv preprint arXiv:2102.03980, 2021. ?" questions, such as "What would be the outcome if we gave this patient treatment t 1 ?". See below for a step-by-step guide for each reported result. Learning disentangled representations for counterfactual regression. available at this link. Simulated data has been used as the input to PrepareData.py which would be followed by the execution of Run.py. The shared layers are trained on all samples. comparison with previous approaches to causal inference from observational You can add new benchmarks by implementing the benchmark interface, see e.g. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. BayesTree: Bayesian additive regression trees. 167302 within the National Research Program (NRP) 75 Big Data. =1(k2)k1i=0i1j=0^PEHE,i,j 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). (2017). To determine the impact of matching fewer than 100% of all samples in a batch, we evaluated PM on News-8 trained with varying percentages of matched samples on the range 0 to 100% in steps of 10% (Figure 4). PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. Counterfactual inference enables one to answer "What if. Besides accounting for the treatment assignment bias, the other major issue in learning for counterfactual inference from observational data is that, given multiple models, it is not trivial to decide which one to select. $ ?>jYJW*9Y!WLPD vu{B" j!P?D ; =?5DEE@?8 7@io$. Causal inference using potential outcomes: Design, modeling, Contributions. 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. Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. Use of the logistic model in retrospective studies. =1(k2)k1i=0i1j=0^ATE,i,jt The topic for this semester at the machine learning seminar was causal inference. Following Imbens (2000); Lechner (2001), we assume unconfoundedness, which consists of three key parts: (1) Conditional Independence Assumption: The assignment to treatment t is independent of the outcome yt given the pre-treatment covariates X, (2) Common Support Assumption: For all values of X, it must be possible to observe all treatments with a probability greater than 0, and (3) Stable Unit Treatment Value Assumption: The observed outcome of any one unit must be unaffected by the assignments of treatments to other units. By modeling the different relations among variables, treatment and outcome, we propose a synergistic learning framework to 1) identify and balance confounders by learning decomposed representation of confounders and non-confounders, and simultaneously 2) estimate the treatment effect in observational studies via counterfactual inference.

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