learning representations for counterfactual inference github

PDF Learning Representations for Counterfactual Inference - arXiv Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates $ @?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 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. Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. stream Children that did not receive specialist visits were part of a control group. 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. Papers With Code is a free resource with all data licensed under. To manage your alert preferences, click on the button below. 370 0 obj Add a We did so by using k head networks, one for each treatment over a set of shared base layers, each with L layers. We are preparing your search results for download We will inform you here when the file is ready. As a Research Staff Member of the Collaborative Research Center on Information Density and Linguistic Encoding, he analyzes cross-level interactions between vector-space representations of linguistic units. 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 In The 22nd International Conference on Artificial Intelligence and Statistics. Once you have completed the experiments, you can calculate the summary statistics (mean +- standard deviation) over all the repeated runs using the. (2016) and consists of 5000 randomly sampled news articles from the NY Times corpus333https://archive.ics.uci.edu/ml/datasets/bag+of+words. }Qm4;)v The shared layers are trained on all samples. Note the installation of rpy2 will fail if you do not have a working R installation on your system (see above). You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. data is confounder identification and balancing. Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. GANITE: Estimation of Individualized Treatment Effects using These k-Nearest-Neighbour (kNN) methods Ho etal. D.Cournapeau, M.Brucher, M.Perrot, and E.Duchesnay. 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. (2016). % /Filter /FlateDecode Bayesian inference of individualized treatment effects using Counterfactual inference enables one to answer "What if?" 367 0 obj 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). The central role of the propensity score in observational studies for causal effects. 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 All rights reserved. Chipman, Hugh and McCulloch, Robert. 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. 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 gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. trees. Under unconfoundedness assumptions, balancing scores have the property that the assignment to treatment is unconfounded given the balancing score Rosenbaum and Rubin (1983); Hirano and Imbens (2004); Ho etal. endstream [2023.04.12]: adding a more detailed sd-webui . We consider a setting in which we are given N i.i.d. A comparison of methods for model selection when estimating 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. We reassigned outcomes and treatments with a new random seed for each repetition. Balancing those We refer to the special case of two available treatments as the binary treatment setting. Article . 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. Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. van der Laan, Mark J and Petersen, Maya L. Causal effect models for realistic individualized treatment and intention to treat rules. Technical report, University of Illinois at Urbana-Champaign, 2008. (2007). decisions. F.Pedregosa, G.Varoquaux, A.Gramfort, V.Michel, B.Thirion, O.Grisel, data that has not been collected in a randomised experiment, on the other hand, is often readily available in large quantities. the treatment effect performs better than the state-of-the-art methods on both 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. Domain adaptation for statistical classifiers. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. On causal and anticausal learning. Scikit-learn: Machine Learning in Python. By modeling the different relations among variables, treatment and outcome, we Morgan, Stephen L and Winship, Christopher. 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. 368 0 obj (3). Bayesian nonparametric modeling for causal inference. Generative Adversarial Nets for inference of Individualised Treatment Effects (GANITE) Yoon etal. In. Recursive partitioning for personalization using observational data. Copyright 2023 ACM, Inc. Learning representations for counterfactual inference. 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 bartMachine: Machine learning with Bayesian additive regression Pi,&t#,RF;NCil6 !M)Ehc! {6&m=>9wB$ We perform extensive experiments on semi-synthetic, real-world data in settings with two and more treatments. compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. (2017), and PD Alaa etal. We found that PM better conforms to the desired behavior than PSMPM and PSMMI. We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. Using balancing scores, we can construct virtually randomised minibatches that approximate the corresponding randomised experiment for the given counterfactual inference task by imputing, for each observed pair of covariates x and factual outcome yt, the remaining unobserved counterfactual outcomes by the outcomes of nearest neighbours in the training data by some balancing score, such as the propensity score. Due to their practical importance, there exists a wide variety of methods for estimating individual treatment effects from observational data. 2C&( ??;9xCc@e%yeym? Flexible and expressive models for learning counterfactual representations that generalise to settings with multiple available treatments could potentially facilitate the derivation of valuable insights from observational data in several important domains, such as healthcare, economics and public policy. (2010); Chipman and McCulloch (2016) and Causal Forests (CF) Wager and Athey (2017). Your search export query has expired. Approximate nearest neighbors: towards removing the curse of in Linguistics and Computation from Princeton University. 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. 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. (2017); Schuler etal. (2017) (Appendix H) to the multiple treatment setting. arXiv as responsive web pages so you Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". an exact match in the balancing score, for observed factual outcomes. MarkR Montgomery, Michele Gragnolati, KathleenA Burke, and Edmundo Paredes. MatchIt: nonparametric preprocessing for parametric causal The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Counterfactual inference enables one to answer "What if?" Although deep learning models have been successfully applied to a variet MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population, Perfect Match: A Simple Method for Learning Representations For We evaluated PM, ablations, baselines, and all relevant state-of-the-art methods: kNN Ho etal. 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. Fredrik Johansson, Uri Shalit, and David Sontag. 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. 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. Examples of representation-balancing methods are Balancing Neural Networks Johansson etal. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. data. Domain adaptation and sample bias correction theory and algorithm for regression. Generative Adversarial Nets. Swaminathan, Adith and Joachims, Thorsten. 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. stream Learning Representations for Counterfactual Inference choice without knowing what would be the feedback for other possible choices. In contrast to existing methods, PM is a simple method that can be used to train expressive non-linear neural network models for ITE estimation from observational data in settings with any number of treatments. 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 Since the original TARNET was limited to the binary treatment setting, we extended the TARNET architecture to the multiple treatment setting (Figure 1). Papers With Code is a free resource with all data licensed under. 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. Hw(a? We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". DanielE Ho, Kosuke Imai, Gary King, and ElizabethA Stuart. 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. endobj dimensionality. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. 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. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. 3) for News-4/8/16 datasets. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. The original experiments reported in our paper were run on Intel CPUs. The topic for this semester at the machine learning seminar was causal inference. Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. 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. Learning representations for counterfactual inference | Proceedings of 369 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). All other results are taken from the respective original authors' manuscripts. (2018) and multiple treatment settings for model selection. 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. Speaker: Clayton Greenberg, Ph.D. You signed in with another tab or window. 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. Pearl, Judea. Causal inference using potential outcomes: Design, modeling, %PDF-1.5 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. Learning representations for counterfactual inference - ICML, 2016. Learning Disentangled Representations for CounterFactual Regression We can not guarantee and have not tested compability with Python 3. RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ The News dataset contains data on the opinion of media consumers on news items. The ATE is not as important as PEHE for models optimised for ITE estimation, but can be a useful indicator of how well an ITE estimator performs at comparing two treatments across the entire population. To run BART, Causal Forests and to reproduce the figures you need to have R installed. The experiments show that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes from observational data. 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. stream Measuring living standards with proxy variables. (2016) to enable the simulation of arbitrary numbers of viewing devices. Interestingly, we found a large improvement over using no matched samples even for relatively small percentages (<40%) of matched samples per batch. 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. %PDF-1.5 We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. << /Filter /FlateDecode /Length 529 >> We can neither calculate PEHE nor ATE without knowing the outcome generating process. Our deep learning algorithm significantly outperforms the previous state-of-the-art. << /Names 366 0 R /OpenAction 483 0 R /Outlines 470 0 R /PageLabels << /Nums [ 0 <> 1 <> 4 <> 5 <> 6 <> 7 <> 11 <> 14 <> 16 <> 20 <> 25 <> 30 <> 32 <> 34 <> 35 <> 39 <> 40 <> 44 <> 49 <> 50 <> 54 <> 57 <> 61 <> 64 <> 65 <> 69 <> 70 <> 77 <> ] >> /PageMode /UseOutlines /Pages 469 0 R /Type /Catalog >> 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. Learning fair representations. Counterfactual inference enables one to answer "What if. In International Conference on Learning Representations. Shalit etal. ,E^-"4nhi/dX]/hs9@A$}M\#6soa0YsR/X#+k!"uqAJ3un>e-I~8@f*M9:3qc'RzH ,` Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. random forests. Causal effect inference with deep latent-variable models. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. (2018) address ITE estimation using counterfactual and ITE generators. Rosenbaum, Paul R and Rubin, Donald B. This repo contains the neural network based counterfactual regression implementation for Ad attribution. 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. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). On the binary News-2, PM outperformed all other methods in terms of PEHE and ATE. (2017) may be used to capture non-linear relationships. Date: February 12, 2020. endobj Are you sure you want to create this branch? that units with similar covariates xi have similar potential outcomes y. Authors: Fredrik D. Johansson. Free Access. Upon convergence, under assumption (1) and for. In literature, this setting is known as the Rubin-Neyman potential outcomes framework Rubin (2005). Susan Athey, Julie Tibshirani, and Stefan Wager. @E)\a6Hk$$x9B]aV`'iuD Accessed: 2016-01-30. He received his M.Sc. ecology. 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. Federated unsupervised representation learning, FITEE, 2022. xcbdg`b`8 $S&`6Ah :H) @DH301?e`%x]0 > ; 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. DanielE Ho, Kosuke Imai, Gary King, ElizabethA Stuart, etal. Identification and estimation of causal effects of multiple The role of the propensity score in estimating dose-response The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. individual treatment effects. How well does PM cope with an increasing treatment assignment bias in the observed data? Rubin, Donald B. Estimating causal effects of treatments in randomized and nonrandomized studies. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and Edit social preview. BayesTree: Bayesian additive regression trees. We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. 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. Estimating individual treatment effects111The ITE is sometimes also referred to as the conditional average treatment effect (CATE). (2017). The script will print all the command line configurations (2400 in total) you need to run to obtain the experimental results to reproduce the News results. We then randomly pick k+1 centroids in topic space, with k centroids zj per viewing device and one control centroid zc. 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). Jinsung Yoon, James Jordon, and Mihaela vander Schaar. 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 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. in Language Science and Technology from Saarland University and his A.B. Quick introduction to CounterFactual Regression (CFR) BART: Bayesian additive regression trees. The script will print all the command line configurations (40 in total) you need to run to obtain the experimental results to reproduce the Jobs results. endobj AhmedM Alaa, Michael Weisz, and Mihaela vander Schaar. Robins, James M, Hernan, Miguel Angel, and Brumback, Babette. Matching methods are among the conceptually simplest approaches to estimating ITEs. 167302 within the National Research Program (NRP) 75 "Big Data". For the python dependencies, see setup.py. The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. (2016). in Linguistics and Computation from Princeton University. i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR Perfect Match: A Simple Method for Learning Representations For How do the learning dynamics of minibatch matching compare to dataset-level matching? A simple method for estimating interactions between a treatment and a large number of covariates. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Bag of words data set. As a secondary metric, we consider the error ATE in estimating the average treatment effect (ATE) Hill (2011). Formally, this approach is, when converged, equivalent to a nearest neighbour estimator for which we are guaranteed to have access to a perfect match, i.e. algorithms. After the experiments have concluded, use. 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. In addition, we assume smoothness, i.e. (2017). Representation Learning: What Is It and How Do You Teach It? Domain adaptation: Learning bounds and algorithms. Batch learning from logged bandit feedback through counterfactual risk minimization. Sign up to our mailing list for occasional updates. 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. 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. https://dl.acm.org/doi/abs/10.5555/3045390.3045708. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. =0 indicates no assignment bias. << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> - Learning-representations-for-counterfactual-inference-. simultaneously 2) estimate the treatment effect in observational studies via 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? PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. 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. multi-task gaussian processes. https://archive.ics.uci.edu/ml/datasets/bag+of+words.
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