Our deep learning algorithm significantly outperforms the previous state-of-the-art. Shalit etal. Date: February 12, 2020. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Cortes, Corinna and Mohri, Mehryar. More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. 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. By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output. Accessed: 2016-01-30. Share on. van der Laan, Mark J and Petersen, Maya L. Causal effect models for realistic individualized treatment and intention to treat rules. "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference." arXiv preprint arXiv:2102.03980, 2021. Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah. Representation Learning. Invited commentary: understanding bias amplification. Jingyu He, Saar Yalov, and P Richard Hahn. Representation Learning: What Is It and How Do You Teach It? %PDF-1.5 (2018), Balancing Neural Network (BNN) Johansson etal. 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. To determine the impact of matching fewer than 100% of all samples in a batch, we evaluated PM on News-8 trained with varying percentages of matched samples on the range 0 to 100% in steps of 10% (Figure 4). Matching methods are among the conceptually simplest approaches to estimating ITEs. accumulation of data in fields such as healthcare, education, employment and Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. (2017) adjusts the regularisation for each sample during training depending on its treatment propensity. The source code for this work is available at https://github.com/d909b/perfect_match. We found that NN-PEHE correlates significantly better with the PEHE than MSE (Figure 2). 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. 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. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. A Simple Method for Learning Representations For Counterfactual (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). The shared layers are trained on all samples. endobj the treatment and some contribute to the outcome. [HJ)mD:K`G?/BPWw(a&ggl }[OvP ps@]TZP?x ;_[YN^0'5 Rosenbaum, Paul R and Rubin, Donald B. Bio: Clayton Greenberg is a Ph.D. PMLR, 2016. We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". (2011) before training a TARNET (Appendix G). NPCI: Non-parametrics for causal inference. Perfect Match: A Simple Method for Learning Representations For Estimation and inference of heterogeneous treatment effects using random forests. questions, such as "What would be the outcome if we gave this patient treatment t1?". in Linguistics and Computation from Princeton University. 373 0 obj << /Type /XRef /Length 73 /Filter /FlateDecode /DecodeParms << /Columns 4 /Predictor 12 >> /W [ 1 2 1 ] /Index [ 367 184 ] /Info 183 0 R /Root 369 0 R /Size 551 /Prev 846568 /ID [<6128b543239fbdadfc73903b5348344b>] >> We evaluated PM, ablations, baselines, and all relevant state-of-the-art methods: kNN Ho etal. Tian, Lu, Alizadeh, Ash A, Gentles, Andrew J, and Tibshirani, Robert. 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. Generative Adversarial Nets for inference of Individualised Treatment Effects (GANITE) Yoon etal. Swaminathan, Adith and Joachims, Thorsten. We also found that the NN-PEHE correlates significantly better with real PEHE than MSE, that including more matched samples in each minibatch improves the learning of counterfactual representations, and that PM handles an increasing treatment assignment bias better than existing state-of-the-art methods. stream A literature survey on domain adaptation of statistical classifiers. We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. Higher values of indicate a higher expected assignment bias depending on yj. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. Perfect Match: A Simple Method for Learning Representations For simultaneously 2) estimate the treatment effect in observational studies via Langford, John, Li, Lihong, and Dudk, Miroslav. (2017). 372 0 obj Counterfactual Inference | Papers With Code Simulated data has been used as the input to PrepareData.py which would be followed by the execution of Run.py. ]|2jZ;lU.t`' RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ These k-Nearest-Neighbour (kNN) methods Ho etal. https://dl.acm.org/doi/abs/10.5555/3045390.3045708. We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. 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. =1(k2)k1i=0i1j=0^ATE,i,jt compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. In, Strehl, Alex, Langford, John, Li, Lihong, and Kakade, Sham M. Learning from logged implicit exploration data. =1(k2)k1i=0i1j=0^PEHE,i,j 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. Identification and estimation of causal effects of multiple 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). Federated unsupervised representation learning, FITEE, 2022. 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. This repo contains the neural network based counterfactual regression implementation for Ad attribution. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Want to hear about new tools we're making? In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. https://cran.r-project.org/package=BayesTree/, 2016. Bottou, Lon, Peters, Jonas, Quinonero-Candela, Joaquin, Charles, Denis X, Chickering, D Max, Portugaly, Elon, Ray, Dipankar, Simard, Patrice, and Snelson, Ed. XBART: Accelerated Bayesian additive regression trees. Counterfactual inference from observational data always requires further assumptions about the data-generating process Pearl (2009); Peters etal. Domain-adversarial training of neural networks. Analogously to Equations (2) and (3), the ^NN-PEHE metric can be extended to the multiple treatment setting by considering the mean ^NN-PEHE between all (k2) possible pairs of treatments (Appendix F). 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. Wager, Stefan and Athey, Susan. medication?". 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 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). 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. Max Welling. observed samples X, where each sample consists of p covariates xi with i[0..p1]. data. the treatment effect performs better than the state-of-the-art methods on both Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . 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. 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. inference. % "Would this patient have lower blood sugar had she received a different The advantage of matching on the minibatch level, rather than the dataset level Ho etal. Robins, James M, Hernan, Miguel Angel, and Brumback, Babette. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. in Linguistics and Computation from Princeton University. 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. 371 0 obj Uri Shalit, FredrikD Johansson, and David Sontag. We can neither calculate PEHE nor ATE without knowing the outcome generating process. available at this link. Following Imbens (2000); Lechner (2001), we assume unconfoundedness, which consists of three key parts: (1) Conditional Independence Assumption: The assignment to treatment t is independent of the outcome yt given the pre-treatment covariates X, (2) Common Support Assumption: For all values of X, it must be possible to observe all treatments with a probability greater than 0, and (3) Stable Unit Treatment Value Assumption: The observed outcome of any one unit must be unaffected by the assignments of treatments to other units. 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). Hw(a? Repeat for all evaluated method / degree of hidden confounding combinations. 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. algorithms. 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. In literature, this setting is known as the Rubin-Neyman potential outcomes framework Rubin (2005). 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. In these situations, methods for estimating causal effects from observational data are of paramount importance. endobj 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. Counterfactual reasoning and learning systems: The example of computational advertising. Correlation analysis of the real PEHE (y-axis) with the mean squared error (MSE; left) and the nearest neighbour approximation of the precision in estimation of heterogenous effect (NN-PEHE; right) across over 20000 model evaluations on the validation set of IHDP. Bang, Heejung and Robins, James M. Doubly robust estimation in missing data and causal inference models. endstream All other results are taken from the respective original authors' manuscripts. 4. Causal inference using potential outcomes: Design, modeling, 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. - Learning-representations-for-counterfactual-inference-. ci0pf=[3@Cm*A,rY`@n 9u_\p=p'h3C'[|kvZMJ:S=9dGC-!43BA RQqr01o:xG ?7>[pM)kC2@p%Np % We repeated experiments on IHDP and News 1000 and 50 times, respectively. CauseBox | Proceedings of the 30th ACM International Conference on \includegraphics[width=0.25]img/nn_pehe. Use of the logistic model in retrospective studies. "7B}GgRvsp;"DD-NK}si5zU`"98}02 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. 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. zz !~A|66}$EPp("i n $* PD, in essence, discounts samples that are far from equal propensity for each treatment during training. endobj Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Besides accounting for the treatment assignment bias, the other major issue in learning for counterfactual inference from observational data is that, given multiple models, it is not trivial to decide which one to select. Our experiments demonstrate that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmarks, particularly in settings with many treatments. task. BayesTree: Bayesian additive regression trees. Learning Disentangled Representations for CounterFactual Regression Negar Hassanpour, Russell Greiner 25 Sep 2019, 12:15 (modified: 11 Mar 2020, 00:33) ICLR 2020 Conference Blind Submission Readers: Everyone Keywords: Counterfactual Regression, Causal Effect Estimation, Selection Bias, Off-policy Learning (2017). Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k 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. Daume III, Hal and Marcu, Daniel. 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. Counterfactual Inference With Neural Networks, Double Robust Representation Learning for Counterfactual Prediction, Enhancing Counterfactual Classification via Self-Training, Interventional and Counterfactual Inference with Diffusion Models, Continual Causal Inference with Incremental Observational Data, Explaining Deep Learning Models using Causal Inference. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Learning representations for counterfactual inference - ICML, 2016. The central role of the propensity score in observational studies for Article . 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). ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. (ITE) from observational data is an important problem in many domains. 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. NPCI: Non-parametrics for causal inference, 2016. /Length 3974 (2017); Schuler etal. The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. This is sometimes referred to as bandit feedback (Beygelzimer et al.,2010). The variational fair auto encoder. (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.
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