Learning Representations for Counterfactual Inference 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. Navigate to the directory containing this file. XBART: Accelerated Bayesian additive regression trees. Our deep learning algorithm significantly outperforms the previous state-of-the-art. endstream Among States that did not Expand Medicaid, CETransformer: Casual Effect Estimation via Transformer Based ^mPEHE 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. Counterfactual inference enables one to answer "What if. (2017); Alaa and Schaar (2018). Bio: Clayton Greenberg is a Ph.D. 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 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, we assume smoothness, i.e. endobj Scatterplots show a subsample of 1400 data points. We did so by using k head networks, one for each treatment over a set of shared base layers, each with L layers. NPCI: Non-parametrics for causal inference, 2016. 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. 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. practical algorithm design. In literature, this setting is known as the Rubin-Neyman potential outcomes framework Rubin (2005). The advantage of matching on the minibatch level, rather than the dataset level Ho etal. 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. 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. We focus on counterfactual questions raised by what areknown asobservational studies. inference which brings together ideas from domain adaptation and representation Weiss, Jeremy C, Kuusisto, Finn, Boyd, Kendrick, Lui, Jie, and Page, David C. Machine learning for treatment assignment: Improving individualized risk attribution. Add a GitHub - ankits0207/Learning-representations-for-counterfactual 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. data is confounder identification and balancing. Evaluating the econometric evaluations of training programs with Generative Adversarial Nets. cq?g The ACM Digital Library is published by the Association for Computing Machinery. ci0pf=[3@Cm*A,rY`@n 9u_\p=p'h3C'[|kvZMJ:S=9dGC-!43BA RQqr01o:xG ?7>[pM)kC2@p%Np Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. The outcomes were simulated using the NPCI package from Dorie (2016)222We used the same simulated outcomes as Shalit etal. After the experiments have concluded, use. 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. You can add new benchmarks by implementing the benchmark interface, see e.g. 370 0 obj 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. 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. confounders, ignoring the identification of confounders and non-confounders. 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). 1 Paper The News dataset contains data on the opinion of media consumers on news items. bartMachine: Machine learning with Bayesian additive regression 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). 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. 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. PMLR, 2016. (2018), Balancing Neural Network (BNN) Johansson etal. We develop performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual treatment effects in the setting with multiple available treatments. 2#w2;0USFJFxp G+=EtA65ztTu=i7}qMX`]vhfw7uD/k^[%_ .r d9mR5GMEe^; :$LZ9&|cvrDTD]Dn@9DZO8=VZe+IjBX{\q Ep8[Cw.M'ZK4b>.R7,&z>@|/:\4w&"sMHNcj7z3GrT |WJ-P4;nn[\wEIwF'E8"Q/JVAj8*k$:l2NsAi:NvmzSKO4gMg?#bYE65lf pAy6s9>->0| >b8%7a/ KqG9cw|w]jIDic. 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. Chipman, Hugh and McCulloch, Robert. We then randomly pick k+1 centroids in topic space, with k centroids zj per viewing device and one control centroid zc. The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. We found that PM better conforms to the desired behavior than PSMPM and PSMMI. 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 $5 per 500 images. Note that we only evaluate PM, + on X, + MLP, PSM on Jobs. Upon convergence, under assumption (1) and for. Tree-based methods train many weak learners to build expressive ensemble models. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . Doubly robust estimation of causal effects. Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. (2007), BART Chipman etal. DanielE Ho, Kosuke Imai, Gary King, ElizabethA Stuart, etal. Max Welling. Repeat for all evaluated percentages of matched samples. Newman, David. A simple method for estimating interactions between a treatment and a large number of covariates. The experiments show that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes from observational data. A Simple Method for Learning Representations For Counterfactual ?" questions, such as "What would be the outcome if we gave this patient treatment t 1 ?". Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k Sign up to our mailing list for occasional updates. non-confounders would generate additional bias for treatment effect estimation. to install the perfect_match package and the python dependencies. Federated unsupervised representation learning, FITEE, 2022. (3). 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 We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. The chosen architecture plays a key role in the performance of neural networks when attempting to learn representations for counterfactual inference Shalit etal. ecology. Inferring the causal effects of interventions is a central pursuit in many important domains, such as healthcare, economics, and public policy. Estimating individual treatment effects111The ITE is sometimes also referred to as the conditional average treatment effect (CATE). Bag of words data set. This setup comes up in diverse areas, for example off-policy evalu-ation in reinforcement learning (Sutton & Barto,1998), The set of available treatments can contain two or more treatments. Rosenbaum, Paul R and Rubin, Donald B. Kevin Xia - GitHub Pages Note that we ran several thousand experiments which can take a while if evaluated sequentially. Doubly robust policy evaluation and learning. Hill, Jennifer L. Bayesian nonparametric modeling for causal inference. Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates 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. Susan Athey, Julie Tibshirani, and Stefan Wager. (2017) adjusts the regularisation for each sample during training depending on its treatment propensity. Conventional machine learning methods, built By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem. This indicates that PM is effective with any low-dimensional balancing score. (2017); Schuler 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. dont have to squint at a PDF. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. arXiv as responsive web pages so you The script will print all the command line configurations (1750 in total) you need to run to obtain the experimental results to reproduce the News results. Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. 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. 167302 within the National Research Program (NRP) 75 Big Data. The central role of the propensity score in observational studies for 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. x4k6Q0z7F56K.HtB$w}s{y_5\{_{? 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. Domain adaptation and sample bias correction theory and algorithm for regression. individual treatment effects. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Want to hear about new tools we're making? Login. 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. Learning disentangled representations for counterfactual regression. comparison with previous approaches to causal inference from observational Please download or close your previous search result export first before starting a new bulk export. We can not guarantee and have not tested compability with Python 3. (2017). 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). See below for a step-by-step guide for each reported result. Bang, Heejung and Robins, James M. Doubly robust estimation in missing data and causal inference models. Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. Your file of search results citations is now ready. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and 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 In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. data. GANITE uses a complex architecture with many hyperparameters and sub-models that may be difficult to implement and optimise. Recursive partitioning for personalization using observational data. Causal Multi-task Gaussian Processes (CMGP) Alaa and vander Schaar (2017) apply a multi-task Gaussian Process to ITE estimation. (2017) (Appendix H) to the multiple treatment setting. While the underlying idea behind PM is simple and effective, it has, to the best of our knowledge, not yet been explored. (2000); Louizos etal. Representation Learning. We report the mean value. that units with similar covariates xi have similar potential outcomes y. The source code for this work is available at https://github.com/d909b/perfect_match. @E)\a6Hk$$x9B]aV`'iuD 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. Scikit-learn: Machine Learning in Python. https://dl.acm.org/doi/abs/10.5555/3045390.3045708. 2) and ^mATE (Eq. Run the command line configurations from the previous step in a compute environment of your choice. 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. Matching as nonparametric preprocessing for reducing model dependence endobj Estimation and inference of heterogeneous treatment effects using Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. Fredrik Johansson, Uri Shalit, and David Sontag. The optimisation of CMGPs involves a matrix inversion of O(n3) complexity that limits their scalability. 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). Jingyu He, Saar Yalov, and P Richard Hahn. /Filter /FlateDecode How well does PM cope with an increasing treatment assignment bias in the observed data? (2016). In Deep counterfactual networks with propensity-dropout. 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 &5mO"}S~2,z3?H BGKxr gOp1b~7Z7A^:12N$PF"=.DTcuT*5(i\C,nZZq+6TR/]FyQo'I)#TFq==UX KgvAZn&W_j3`"e|>n( We perform extensive experiments on semi-synthetic, real-world data in settings with two and more treatments. PDF Learning Representations for Counterfactual Inference - arXiv in parametric causal inference. 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. Quick introduction to CounterFactual Regression (CFR) This repo contains the neural network based counterfactual regression implementation for Ad attribution. Or, have a go at fixing it yourself the renderer is open source! (2016) and consists of 5000 randomly sampled news articles from the NY Times corpus333https://archive.ics.uci.edu/ml/datasets/bag+of+words. << /Linearized 1 /L 849041 /H [ 2447 819 ] /O 371 /E 54237 /N 78 /T 846567 >> 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. [width=0.25]img/mse https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. Children that did not receive specialist visits were part of a control group. Jinsung Yoon, James Jordon, and Mihaela vander Schaar. Batch learning from logged bandit feedback through counterfactual risk minimization. 1) and ATE (Appendix B) for the binary IHDP and News-2 datasets, and the ^mPEHE (Eq. Finally, although TARNETs trained with PM have similar asymptotic properties as kNN, we found that TARNETs trained with PM significantly outperformed kNN in all cases. 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. However, one can inspect the pair-wise PEHE to obtain the whole picture. 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.