endobj 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. PM is easy to use with existing neural network architectures, simple to implement, and does not add any hyperparameters or computational complexity. Bottou, Lon, Peters, Jonas, Quinonero-Candela, Joaquin, Charles, Denis X, Chickering, D Max, Portugaly, Elon, Ray, Dipankar, Simard, Patrice, and Snelson, Ed. 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. Free Access. endstream 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. This regularises the treatment assignment bias but also introduces data sparsity as not all available samples are leveraged equally for training. The topic for this semester at the machine learning seminar was causal inference. Domain adaptation and sample bias correction theory and algorithm for regression. Papers With Code is a free resource with all data licensed under. For the IHDP and News datasets we respectively used 30 and 10 optimisation runs for each method using randomly selected hyperparameters from predefined ranges (Appendix I). Technical report, University of Illinois at Urbana-Champaign, 2008. stream The primary metric that we optimise for when training models to estimate ITE is the PEHE Hill (2011). 2019. stream Please download or close your previous search result export first before starting a new bulk export. 368 0 obj Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. A tag already exists with the provided branch name. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 367 0 obj One fundamental problem in the learning treatment effect from observational 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. endstream Examples of tree-based methods are Bayesian Additive Regression Trees (BART) Chipman 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. We develop performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual treatment effects in the setting with multiple available treatments. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. All datasets with the exception of IHDP were split into a training (63%), validation (27%) and test set (10% of samples). 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. M.Blondel, P.Prettenhofer, R.Weiss, V.Dubourg, J.Vanderplas, A.Passos, Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> 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. We consider the task of answering counterfactual questions such as, CSE, Chalmers University of Technology, Gteborg, Sweden . 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]. 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. treatments under the conditional independence assumption. In addition to a theoretical justification, we perform an empirical We presented PM, a new and simple method for training neural networks for estimating ITEs from observational data that extends to any number of available treatments. (ITE) from observational data is an important problem in many domains. PD, in essence, discounts samples that are far from equal propensity for each treatment during training. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. Chernozhukov, Victor, Fernndez-Val, Ivn, and Melly, Blaise. Jonas Peters, Dominik Janzing, and Bernhard Schlkopf. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). Learning representations for counterfactual inference - ICML, 2016. << /Annots [ 484 0 R ] /Contents 372 0 R /MediaBox [ 0 0 362.835 272.126 ] /Parent 388 0 R /Resources 485 0 R /Trans << /S /R >> /Type /Page >> He received his M.Sc. counterfactual inference. Please try again. Causal inference using potential outcomes: Design, modeling, In International Conference on Learning Representations. method can precisely identify and balance confounders, while the estimation of i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. endstream (2007). 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. observed samples X, where each sample consists of p covariates xi with i[0..p1]. We therefore suggest to run the commands in parallel using, e.g., a compute cluster. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. << /Linearized 1 /L 849041 /H [ 2447 819 ] /O 371 /E 54237 /N 78 /T 846567 >> xZY~S[!-"v].8 g9^|94>nKW{[/_=_U{QJUE8>?j+du(KV7>y+ya https://cran.r-project.org/package=BayesTree/, 2016. 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. Propensity Dropout (PD) Alaa etal. !lTv[ sj 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). Learning Representations for Counterfactual Inference choice without knowing what would be the feedback for other possible choices. We calculated the PEHE (Eq. Our deep learning algorithm significantly outperforms the previous You can also reproduce the figures in our manuscript by running the R-scripts in. We used four different variants of this dataset with k=2, 4, 8, and 16 viewing devices, and =10, 10, 10, and 7, respectively. the treatment and some contribute to the outcome. Pi,&t#,RF;NCil6 !M)Ehc! 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 . This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. In the first part of this talk, I will present my completed and ongoing work on how computers can learn useful representations of linguistic units, especially in the case in which units at different levels, such as a word and the underlying event it describes, must work together within a speech recognizer, translator, or search engine. stream We found that NN-PEHE correlates significantly better with the PEHE than MSE (Figure 2). ]|2jZ;lU.t`' We also evaluated preprocessing the entire training set with PSM using the same matching routine as PM (PSMPM) and the "MatchIt" package (PSMMI, Ho etal. Domain-adversarial training of neural networks. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. The role of the propensity score in estimating dose-response DanielE Ho, Kosuke Imai, Gary King, ElizabethA Stuart, etal. A simple method for estimating interactions between a treatment and a large number of covariates. PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. (2017); Alaa and Schaar (2018). zz !~A|66}$EPp("i n $* We use cookies to ensure that we give you the best experience on our website. [width=0.25]img/mse 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. 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 distribution of samples may therefore differ significantly between the treated group and the overall population. While the underlying idea behind PM is simple and effective, it has, to the best of our knowledge, not yet been explored. (2017) adjusts the regularisation for each sample during training depending on its treatment propensity. Matching as nonparametric preprocessing for reducing model dependence 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. decisions. The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. https://archive.ics.uci.edu/ml/datasets/bag+of+words. Candidate at the Saarland University Graduate School of Computer Science, where he is advised by Dietrich Klakow. Approximate nearest neighbors: towards removing the curse of 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. (2011), is that it reduces the variance during training which in turn leads to better expected performance for counterfactual inference (Appendix E). Edit social preview. Assessing the Gold Standard Lessons from the History of RCTs.

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