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. Copyright 2023 ACM, Inc. Learning representations for counterfactual inference. Our deep learning algorithm significantly outperforms the previous state-of-the-art. In thispaper we propose a method to learn representations suitedfor counterfactual inference, and show its efcacy in bothsimulated and real world tasks. We outline the Perfect Match (PM) algorithm in Algorithm 1 (complexity analysis and implementation details in Appendix D). ecology. MarkR Montgomery, Michele Gragnolati, KathleenA Burke, and Edmundo Paredes. Ben-David, Shai, Blitzer, John, Crammer, Koby, Pereira, Fernando, et al. Note: Create a results directory before executing Run.py. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. In literature, this setting is known as the Rubin-Neyman potential outcomes framework Rubin (2005). The primary metric that we optimise for when training models to estimate ITE is the PEHE Hill (2011). Estimation and inference of heterogeneous treatment effects using available at this link. We refer to the special case of two available treatments as the binary treatment setting. Doubly robust policy evaluation and learning. We used four different variants of this dataset with k=2, 4, 8, and 16 viewing devices, and =10, 10, 10, and 7, respectively. 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. Once you have completed the experiments, you can calculate the summary statistics (mean +- standard deviation) over all the repeated runs using the. https://github.com/vdorie/npci, 2016. 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. 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. Due to their practical importance, there exists a wide variety of methods for estimating individual treatment effects from observational data. endstream Austin, Peter C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. (2018) and multiple treatment settings for model selection. stream - Learning-representations-for-counterfactual-inference-. treatments under the conditional independence assumption. 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). By modeling the different relations among variables, treatment and outcome, we data is confounder identification and balancing. BART: Bayesian additive regression trees. }Qm4;)v 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. Note that we ran several thousand experiments which can take a while if evaluated sequentially. Add a 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. endstream "Would this patient have lower blood sugar had she received a different 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. 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. !lTv[ sj 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. In these situations, methods for estimating causal effects from observational data are of paramount importance. state-of-the-art. D.Cournapeau, M.Brucher, M.Perrot, and E.Duchesnay. Domain adaptation: Learning bounds and algorithms. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. The optimisation of CMGPs involves a matrix inversion of O(n3) complexity that limits their scalability. NPCI: Non-parametrics for causal inference. the treatment and some contribute to the outcome. 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. PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. 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. Edit social preview. =1(k2)k1i=0i1j=0^PEHE,i,j task. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. The advantage of matching on the minibatch level, rather than the dataset level Ho etal. Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". Jonas Peters, Dominik Janzing, and Bernhard Schlkopf. comparison with previous approaches to causal inference from observational medication?". 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). endobj (2010); Chipman and McCulloch (2016) and Causal Forests (CF) Wager and Athey (2017). In TARNET, the jth head network is only trained on samples from treatment tj. Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. Edit social preview. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. 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. 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. 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). We did so by using k head networks, one for each treatment over a set of shared base layers, each with L layers. He received his M.Sc. Accessed: 2016-01-30. 373 0 obj Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. See below for a step-by-step guide for each reported result. Estimation and inference of heterogeneous treatment effects using random forests. Generative Adversarial Nets. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. AhmedM Alaa, Michael Weisz, and Mihaela vander Schaar. compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. Comparison of the learning dynamics during training (normalised training epochs; from start = 0 to end = 100 of training, x-axis) of several matching-based methods on the validation set of News-8. His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. Repeat for all evaluated methods / levels of kappa combinations. We develop performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual treatment effects in the setting with multiple available treatments. Max Welling. RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ Shalit etal. In. "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference." arXiv preprint arXiv:2102.03980, 2021. inference. 0 qA0)#@K5Ih-X8oYH>2{wB2(k`:0P}U)j|B5z.O{?T ;?eKS+9S!9GQAMTl/! By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output. dimensionality. Domain adaptation and sample bias correction theory and algorithm for regression. Login. In addition, we extended the TARNET architecture and the PEHE metric to settings with more than two treatments, and introduced a nearest neighbour approximation of PEHE and mPEHE that can be used for model selection without having access to counterfactual outcomes. PM is easy to implement, r/WI7FW*^e~gNdk}4]iE3it0W}]%Cw5"$HhKxYlR&{Y_{R~MkE}R0#~8$LVDt*EG_Q hMZk5jCNm1Y%i8vb3 E8&R/g2}h%X7.jR*yqmEi|[$/?XBo{{kSjWIlW Estimating individual treatment effects111The ITE is sometimes also referred to as the conditional average treatment effect (CATE). /Length 3974 in Linguistics and Computation from Princeton University. 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. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. 367 0 obj 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. synthetic and real-world datasets. https://dl.acm.org/doi/abs/10.5555/3045390.3045708. Bottou, Lon, Peters, Jonas, Quinonero-Candela, Joaquin, Charles, Denis X, Chickering, D Max, Portugaly, Elon, Ray, Dipankar, Simard, Patrice, and Snelson, Ed. Recursive partitioning for personalization using observational data. Note the installation of rpy2 will fail if you do not have a working R installation on your system (see above). PM may be used for settings with any amount of treatments, is compatible with any existing neural network architecture, simple to implement, and does not introduce any additional hyperparameters or computational complexity. << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> $ ?>jYJW*9Y!WLPD vu{B" j!P?D ; =?5DEE@?8 7@io$. observed samples X, where each sample consists of p covariates xi with i[0..p1]. ^mATE Come up with a framework to train models for factual and counterfactual inference. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. The central role of the propensity score in observational studies for To run the IHDP benchmark, you need to download the raw IHDP data folds as used by Johanson et al. This indicates that PM is effective with any low-dimensional balancing score. 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. Assessing the Gold Standard Lessons from the History of RCTs. 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. You signed in with another tab or window. Candidate at the Saarland University Graduate School of Computer Science, where he is advised by Dietrich Klakow. 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 algorithms. (2010); Chipman and McCulloch (2016), Random Forests (RF) Breiman (2001), CF Wager and Athey (2017), GANITE Yoon etal. Counterfactual inference enables one to answer "What if?" We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. CSE, Chalmers University of Technology, Gteborg, Sweden . stream [Takeuchi et al., 2021] Takeuchi, Koh, et al. 2) and ^mATE (Eq. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. The variational fair auto encoder. Chengyuan Liu, Leilei Gan, Kun Kuang*, Fei Wu. /Filter /FlateDecode 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 . Analysis of representations for domain adaptation. This work was partially funded by the Swiss National Science Foundation (SNSF) project No. %PDF-1.5 KO{J4X>+nv^m.U_B;K'pr4])||&ha~2/r5vg9(uT7uo%ztr',a3dZX.6"{3 `1QkP "n3^}. Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. Interestingly, we found a large improvement over using no matched samples even for relatively small percentages (<40%) of matched samples per batch. Langford, John, Li, Lihong, and Dudk, Miroslav. This work was partially funded by the Swiss National Science Foundation (SNSF) project No. Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. 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. Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. Home Browse by Title Proceedings ICML'16 Learning representations for counterfactual inference. experimental data. All rights reserved. questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. DanielE Ho, Kosuke Imai, Gary King, ElizabethA Stuart, etal. functions. 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. PMLR, 2016. (2016). 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 The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. 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. Counterfactual inference enables one to answer "What if?" 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. 370 0 obj Examples of tree-based methods are Bayesian Additive Regression Trees (BART) Chipman etal. Authors: Fredrik D. Johansson. Most of the previous methods We evaluated PM, ablations, baselines, and all relevant state-of-the-art methods: kNN Ho etal. Share on Approximate nearest neighbors: towards removing the curse of (2018), Balancing Neural Network (BNN) Johansson 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. To run BART, you need to have the R-packages, To run Causal Forests, you need to have the R-package, To reproduce the paper's figures, you need to have the R-package. individual treatment effects. the treatment effect performs better than the state-of-the-art methods on both https://archive.ics.uci.edu/ml/datasets/bag+of+words. On IHDP, the PM variants reached the best performance in terms of PEHE, and the second best ATE after CFRNET. PSMMI was overfitting to the treated group. random forests. Both PEHE and ATE can be trivially extended to multiple treatments by considering the average PEHE and ATE between every possible pair of treatments. The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. (2) (2000); Louizos etal. The distribution of samples may therefore differ significantly between the treated group and the overall population. Counterfactual inference enables one to answer "What if. We consider the task of answering counterfactual questions such as, Propensity Dropout (PD) Alaa etal. Propensity Dropout (PD) Alaa etal. 2C&( ??;9xCc@e%yeym? Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. Note that we lose the information about the precision in estimating ITE between specific pairs of treatments by averaging over all (k2) pairs. We focus on counterfactual questions raised by what areknown asobservational studies. To rectify this problem, we use a nearest neighbour approximation ^NN-PEHE of the ^PEHE metric for the binary Shalit etal. Our deep learning algorithm significantly outperforms the previous Create a folder to hold the experimental results. Observational studies are rising in importance due to the widespread 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. (2007), BART Chipman etal. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. However, current methods for training neural networks for counterfactual . 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 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. Measuring living standards with proxy variables. 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. 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). XBART: Accelerated Bayesian additive regression trees. 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. 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. Simulated data has been used as the input to PrepareData.py which would be followed by the execution of Run.py. van der Laan, Mark J and Petersen, Maya L. Causal effect models for realistic individualized treatment and intention to treat rules. Wager, Stefan and Athey, Susan. Given the training data with factual outcomes, we wish to train a predictive model ^f that is able to estimate the entire potential outcomes vector ^Y with k entries ^yj.
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