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. This is sometimes referred to as bandit feedback (Beygelzimer et al.,2010). We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. x4k6Q0z7F56K.HtB$w}s{y_5\{_{? 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. 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. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. See https://www.r-project.org/ for installation instructions. << /Filter /FlateDecode /Length 529 >> %PDF-1.5 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. data that has not been collected in a randomised experiment, on the other hand, is often readily available in large quantities. 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. Share on 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. 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. To manage your alert preferences, click on the button below. (2011) to estimate p(t|X) for PM on the training set. Generative Adversarial Nets. 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 from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. The source code for this work is available at https://github.com/d909b/perfect_match. 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. endstream questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". A literature survey on domain adaptation of statistical classifiers. Fredrik Johansson, Uri Shalit, and David Sontag. Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. ;'/ trees. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Counterfactual reasoning and learning systems: The example of computational advertising. Doubly robust policy evaluation and learning. MicheleJonsson Funk, Daniel Westreich, Chris Wiesen, Til Strmer, M.Alan that units with similar covariates xi have similar potential outcomes y. 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. https://archive.ics.uci.edu/ml/datasets/bag+of+words. We found that including more matches indeed consistently reduces the counterfactual error up to 100% of samples matched. Note: Create a results directory before executing Run.py. Our deep learning algorithm significantly outperforms the previous state-of-the-art. Bayesian inference of individualized treatment effects using (2018) address ITE estimation using counterfactual and ITE generators. 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. On IHDP, the PM variants reached the best performance in terms of PEHE, and the second best ATE after CFRNET. @E)\a6Hk$$x9B]aV`'iuD We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. Gani, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, Franois, Marchand, Mario, and Lempitsky, Victor. We use cookies to ensure that we give you the best experience on our website. The ATE measures the average difference in effect across the whole population (Appendix B). (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. Bio: Clayton Greenberg is a Ph.D. Newman, David. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. 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. - Learning-representations-for-counterfactual-inference-. Ben-David, Shai, Blitzer, John, Crammer, Koby, Pereira, Fernando, et al. 3) for News-4/8/16 datasets. The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. The propensity score with continuous treatments. KO{J4X>+nv^m.U_B;K'pr4])||&ha~2/r5vg9(uT7uo%ztr',a3dZX.6"{3 `1QkP "n3^}. ^mPEHE For each sample, the potential outcomes are represented as a vector Y with k entries yj where each entry corresponds to the outcome when applying one treatment tj out of the set of k available treatments T={t0,,tk1} with j[0..k1]. A kernel two-sample test. Accessed: 2016-01-30. Doubly robust policy evaluation and learning. Sign up to our mailing list for occasional updates. You can look at the slides here. stream Domain adaptation for statistical classifiers. 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. causes of both the treatment and the outcome, some variables only contribute to Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". counterfactual inference. In the first part of this talk, I will present my completed and ongoing work on how computers can learn useful representations of linguistic units, especially in the case in which units at different levels, such as a word and the underlying event it describes, must work together within a speech recognizer, translator, or search engine. Candidate at the Saarland University Graduate School of Computer Science, where he is advised by Dietrich Klakow. 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. 1 Paper All datasets with the exception of IHDP were split into a training (63%), validation (27%) and test set (10% of samples). << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> The topic for this semester at the machine learning seminar was causal inference. Learning representations for counterfactual inference. Causal inference using potential outcomes: Design, modeling, 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). In, All Holdings within the ACM Digital Library. (2017) adjusts the regularisation for each sample during training depending on its treatment propensity. BayesTree: Bayesian additive regression trees. 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. (2017) that use different metrics such as the Wasserstein distance. For everything else, email us at [emailprotected]. We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. 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. 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. Small software tool to analyse search results on twitter to highlight counterfactual statements on certain topics, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. Bayesian nonparametric modeling for causal inference. Tian, Lu, Alizadeh, Ash A, Gentles, Andrew J, and Tibshirani, Robert. accumulation of data in fields such as healthcare, education, employment and Recursive partitioning for personalization using observational data. To judge whether NN-PEHE is more suitable for model selection for counterfactual inference than MSE, we compared their respective correlations with the PEHE on IHDP. Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. (2016) that attempt to find such representations by minimising the discrepancy distance Mansour etal. A tag already exists with the provided branch name. BART: Bayesian additive regression trees. ITE estimation from observational data is difficult for two reasons: Firstly, we never observe all potential outcomes. Besides accounting for the treatment assignment bias, the other major issue in learning for counterfactual inference from observational data is that, given multiple models, it is not trivial to decide which one to select. ,E^-"4nhi/dX]/hs9@A$}M\#6soa0YsR/X#+k!"uqAJ3un>e-I~8@f*M9:3qc'RzH ,` stream (2017).. Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . We reassigned outcomes and treatments with a new random seed for each repetition. Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. (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. (2017), and PD Alaa etal. 367 0 obj RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ PMLR, 1130--1138. Treatment effect estimation with disentangled latent factors, Adversarial De-confounding in Individualised Treatment Effects We extended the original dataset specification in Johansson etal. 369 0 obj Generative Adversarial Nets for inference of Individualised Treatment Effects (GANITE) Yoon etal. task. simultaneously 2) estimate the treatment effect in observational studies via 167302 within the National Research Program (NRP) 75 Big Data. Tree-based methods train many weak learners to build expressive ensemble models. 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. Come up with a framework to train models for factual and counterfactual inference. Learning representations for counterfactual inference. He received his M.Sc. arXiv as responsive web pages so you comparison with previous approaches to causal inference from observational 4. Rubin, Donald B. Causal inference using potential outcomes. The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. The distribution of samples may therefore differ significantly between the treated group and the overall population. 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. 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. Several new mode, eg, still mode, reference mode, resize mode are online for better and custom applications.. Happy to see more community demos at bilibili, Youtube and twitter #sadtalker.. Changelog (Previous changelog can be founded here) [2023.04.15]: Adding automatic1111 colab by @camenduru, thanks for this awesome colab: . 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. PD, in essence, discounts samples that are far from equal propensity for each treatment during training. This shows that propensity score matching within a batch is indeed effective at improving the training of neural networks for counterfactual inference. Rosenbaum, Paul R and Rubin, Donald B. 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 rectify this problem, we use a nearest neighbour approximation ^NN-PEHE of the ^PEHE metric for the binary Shalit etal. If you find a rendering bug, file an issue on GitHub. In literature, this setting is known as the Rubin-Neyman potential outcomes framework Rubin (2005). 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 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. 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. PSMMI was overfitting to the treated group. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. We report the mean value. Wager, Stefan and Athey, Susan. This regularises the treatment assignment bias but also introduces data sparsity as not all available samples are leveraged equally for training. (2016). 370 0 obj Domain adaptation: Learning bounds and algorithms. Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k LauraE. Bothwell, JeremyA. Greene, ScottH. Podolsky, and DavidS. Jones. decisions. Your results should match those found in the. (2011). 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. Your search export query has expired. In thispaper we propose a method to learn representations suitedfor counterfactual inference, and show its efcacy in bothsimulated and real world tasks. Papers With Code is a free resource with all data licensed under. endstream available at this link. Scatterplots show a subsample of 1400 data points. We can neither calculate PEHE nor ATE without knowing the outcome generating process. MarkR Montgomery, Michele Gragnolati, KathleenA Burke, and Edmundo Paredes. 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. PM is easy to implement, (2018) and multiple treatment settings for model selection. 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). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (2010); Chipman and McCulloch (2016) and Causal Forests (CF) Wager and Athey (2017). Propensity Dropout (PD) Alaa etal. For the python dependencies, see setup.py. 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 set of available treatments can contain two or more treatments. More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. 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. 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. (2016) and consists of 5000 randomly sampled news articles from the NY Times corpus333https://archive.ics.uci.edu/ml/datasets/bag+of+words. (2017). ecology. In the binary setting, the PEHE measures the ability of a predictive model to estimate the difference in effect between two treatments t0 and t1 for samples X. However, one can inspect the pair-wise PEHE to obtain the whole picture. (3). Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. (2007), BART Chipman etal. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and /Length 3974 Jiang, Jing. 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. As an Adjunct Lecturer (Lehrbeauftragter) of the Computer Science, and Language Science and Technology departments, he teaches courses on Methods of Mathematical Analysis, Probability Theory, Syntactic Theory, and Computational Linguistics. 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. PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. 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 the IHDP benchmark, you need to download the raw IHDP data folds as used by Johanson et al. (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). As a secondary metric, we consider the error ATE in estimating the average treatment effect (ATE) Hill (2011). 2) and ^mATE (Eq. To run BART, Causal Forests and to reproduce the figures you need to have R installed. The outcomes were simulated using the NPCI package from Dorie (2016)222We used the same simulated outcomes as Shalit etal. 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. Propensity Dropout (PD) Alaa etal. Estimation and inference of heterogeneous treatment effects using inference. 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. 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 We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework, EMNLP, 2022. % 2C&( ??;9xCc@e%yeym? >> Examples of representation-balancing methods are Balancing Neural Networks Johansson etal. (2017); Schuler etal. NPCI: Non-parametrics for causal inference, 2016. 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. Jingyu He, Saar Yalov, and P Richard Hahn. In addition to a theoretical justification, we perform an empirical 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. On causal and anticausal learning. The News dataset contains data on the opinion of media consumers on news items. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. GANITE: Estimation of Individualized Treatment Effects using PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. (2009) between treatment groups, and Counterfactual Regression Networks (CFRNET) Shalit etal. "7B}GgRvsp;"DD-NK}si5zU`"98}02 Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. endobj Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. (2017); Alaa and Schaar (2018). Accessed: 2016-01-30. (2007). stream Due to their practical importance, there exists a wide variety of methods for estimating individual treatment effects from observational data. Uri Shalit, FredrikD Johansson, and David Sontag. smartphone, tablet, desktop, television or others Johansson etal. 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. Learning representations for counterfactual inference - ICML, 2016. 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). Since the original TARNET was limited to the binary treatment setting, we extended the TARNET architecture to the multiple treatment setting (Figure 1). Note that we lose the information about the precision in estimating ITE between specific pairs of treatments by averaging over all (k2) pairs. Edit social preview. learning. Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. Estimation and inference of heterogeneous treatment effects using random forests. The ^NN-PEHE estimates the treatment effect of a given sample by substituting the true counterfactual outcome with the outcome yj from a respective nearest neighbour NN matched on X using the Euclidean distance. 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. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. The variational fair auto encoder. Add a [Takeuchi et al., 2021] Takeuchi, Koh, et al. Our empirical results demonstrate that the proposed to install the perfect_match package and the python dependencies. observed samples X, where each sample consists of p covariates xi with i[0..p1]. !lTv[ sj The central role of the propensity score in observational studies for }Qm4;)v i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR causal effects. (2017), Counterfactual Regression Network using the Wasserstein regulariser (CFRNETWass) Shalit etal. Are you sure you want to create this branch? 36 0 obj << How well does PM cope with an increasing treatment assignment bias in the observed data? 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. (2017) subsequently introduced the TARNET architecture to rectify this issue. Home Browse by Title Proceedings ICML'16 Learning representations for counterfactual inference. in parametric causal inference. Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. We develop performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual treatment effects in the setting with multiple available treatments. This indicates that PM is effective with any low-dimensional balancing score. A tag already exists with the provided branch name. We did so by using k head networks, one for each treatment over a set of shared base layers, each with L layers. We consider a setting in which we are given N i.i.d. PM and the presented experiments are described in detail in our paper. (2011). (2007). 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. XBART: Accelerated Bayesian additive regression trees. /Filter /FlateDecode E A1 ha!O5 gcO w.M8JP ? arXiv Vanity renders academic papers from random forests. How do the learning dynamics of minibatch matching compare to dataset-level matching? Use of the logistic model in retrospective studies. You can add new benchmarks by implementing the benchmark interface, see e.g. https://github.com/vdorie/npci, 2016. (2017). PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. 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. Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah. Learning disentangled representations for counterfactual regression. Domain adaptation: Learning bounds and algorithms. ^mATE Observational data, i.e. Our deep learning algorithm significantly outperforms the previous Free Access. 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. 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).