Counterfactual inference enables one to answer "What if. More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. decisions. ^mATE In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. Wager, Stefan and Athey, Susan. The source code for this work is available at https://github.com/d909b/perfect_match. Home Browse by Title Proceedings ICML'16 Learning representations for counterfactual inference. By modeling the different relations among variables, treatment and outcome, we We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. 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. 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 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. Edit social preview. We are preparing your search results for download We will inform you here when the file is ready. How do the learning dynamics of minibatch matching compare to dataset-level matching? For the python dependencies, see setup.py. Learning-representations-for-counterfactual-inference-MyImplementation. Want to hear about new tools we're making? Sign up to our mailing list for occasional updates. accumulation of data in fields such as healthcare, education, employment and 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. Speaker: Clayton Greenberg, Ph.D. Deep counterfactual networks with propensity-dropout. zz !~A|66}$EPp("i n $* (2017). Domain adaptation: Learning bounds and algorithms. r/WI7FW*^e~gNdk}4]iE3it0W}]%Cw5"$HhKxYlR&{Y_{R~MkE}R0#~8$LVDt*EG_Q hMZk5jCNm1Y%i8vb3 E8&R/g2}h%X7.jR*yqmEi|[$/?XBo{{kSjWIlW Cortes, Corinna and Mohri, Mehryar. Papers With Code is a free resource with all data licensed under. A comparison of methods for model selection when estimating NPCI: Non-parametrics for causal inference. We can neither calculate PEHE nor ATE without knowing the outcome generating process. 1 Paper Learning Representations for Counterfactual Inference 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. Matching methods are among the conceptually simplest approaches to estimating ITEs. The ATE measures the average difference in effect across the whole population (Appendix B). Does model selection by NN-PEHE outperform selection by factual MSE? Please try again. Counterfactual inference enables one to answer "What if?" Add a Ben-David, Shai, Blitzer, John, Crammer, Koby, Pereira, Fernando, et al. 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. This repo contains the neural network based counterfactual regression implementation for Ad attribution. The chosen architecture plays a key role in the performance of neural networks when attempting to learn representations for counterfactual inference Shalit etal. You can also reproduce the figures in our manuscript by running the R-scripts in. Kang, Joseph DY and Schafer, Joseph L. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. 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 In Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. 167302 within the National Research Program (NRP) 75 Big Data. Estimating individual treatment effects111The ITE is sometimes also referred to as the conditional average treatment effect (CATE). The outcomes were simulated using the NPCI package from Dorie (2016)222We used the same simulated outcomes as Shalit etal. In, Strehl, Alex, Langford, John, Li, Lihong, and Kakade, Sham M. Learning from logged implicit exploration data. However, one can inspect the pair-wise PEHE to obtain the whole picture. 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. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. PDF Learning Representations for Counterfactual Inference - arXiv (2011) before training a TARNET (Appendix G). confounders, ignoring the identification of confounders and non-confounders. inference which brings together ideas from domain adaptation and representation 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. Are you sure you want to create this branch? (2007). Our deep learning algorithm significantly outperforms the previous =1(k2)k1i=0i1j=0^ATE,i,jt that units with similar covariates xi have similar potential outcomes y. 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. data that has not been collected in a randomised experiment, on the other hand, is often readily available in large quantities. We consider the task of answering counterfactual questions such as, Authors: Fredrik D. Johansson. Free Access. Invited commentary: understanding bias amplification. The advantage of matching on the minibatch level, rather than the dataset level Ho etal. Domain adaptation and sample bias correction theory and algorithm for regression. All rights reserved. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. Check if you have access through your login credentials or your institution to get full access on this article. inference. 0 qA0)#@K5Ih-X8oYH>2{wB2(k`:0P}U)j|B5z.O{?T ;?eKS+9S!9GQAMTl/! Learning representations for counterfactual inference non-confounders would generate additional bias for treatment effect estimation. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. {6&m=>9wB$ In literature, this setting is known as the Rubin-Neyman potential outcomes framework Rubin (2005). See https://www.r-project.org/ for installation instructions. Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks d909b/perfect_match ICLR 2019 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. Rubin, Donald B. Estimating causal effects of treatments in randomized and nonrandomized studies. 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. ^mPEHE treatments under the conditional independence assumption. in Linguistics and Computation from Princeton University. 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. @E)\a6Hk$$x9B]aV`'iuD ecology. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. 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. BayesTree: Bayesian additive regression trees. Learning Representations for Counterfactual Inference In International Conference on Learning Representations. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. - Learning-representations-for-counterfactual-inference-. Bag of words data set. 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). 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. In The 22nd International Conference on Artificial Intelligence and Statistics. We found that PM better conforms to the desired behavior than PSMPM and PSMMI. Representation Learning: What Is It and How Do You Teach It? We calculated the PEHE (Eq. Counterfactual reasoning and learning systems: The example of computational advertising. $ @?g7F1Q./bA!/g[Ee TEOvuJDF QDzF5O2TP?5+7WW]zBVR!vBZ/j#F y2"o|4ll{b33p>i6MwE/q {B#uXzZM;bXb(:#aJCeocD?gb]B<7%{jb0r ;oZ1KZ(OZ2[)k0"1S]^L4Yh-gp g|XK`$QCj 30G{$mt Repeat for all evaluated method / benchmark combinations. xZY~S[!-"v].8 g9^|94>nKW{[/_=_U{QJUE8>?j+du(KV7>y+ya For high-dimensional datasets, the scalar propensity score is preferable because it avoids the curse of dimensionality that would be associated with matching on the potentially high-dimensional X directly. dont have to squint at a PDF. (2007). endobj The ACM Digital Library is published by the Association for Computing Machinery. For IHDP we used exactly the same splits as previously used by Shalit etal. Daume III, Hal and Marcu, Daniel. Marginal structural models and causal inference in epidemiology. 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. The central role of the propensity score in observational studies for }Qm4;)v Learning representations for counterfactual inference. Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. The IHDP dataset is biased because the treatment groups had a biased subset of the treated population removed Shalit etal. However, current methods for training neural networks for counterfactual . PMLR, 1130--1138. Learning Representations for Counterfactual Inference | OpenReview in parametric causal inference. 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. 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. 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. 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. ,E^-"4nhi/dX]/hs9@A$}M\#6soa0YsR/X#+k!"uqAJ3un>e-I~8@f*M9:3qc'RzH ,` 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. We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. Learning representations for counterfactual inference - ICML, 2016. "Learning representations for counterfactual inference." International conference on machine learning. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. To perform counterfactual inference, we require knowledge of the underlying. 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]. Edit social preview. Fredrik Johansson, Uri Shalit, and David Sontag. Observational studies are rising in importance due to the widespread Repeat for all evaluated method / degree of hidden confounding combinations. 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). PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. (2017). Pearl, Judea. counterfactual inference. % Learning Representations for Counterfactual Inference | DeepAI (2017).. Doubly robust estimation of causal effects. 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. dimensionality. [2023.04.12]: adding a more detailed sd-webui . (2017) adjusts the regularisation for each sample during training depending on its treatment propensity. 370 0 obj 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. Interestingly, we found a large improvement over using no matched samples even for relatively small percentages (<40%) of matched samples per batch. Domain adaptation for statistical classifiers. Note that we only evaluate PM, + on X, + MLP, PSM on Jobs. For low-dimensional datasets, the covariates X are a good default choice as their use does not require a model of treatment propensity. 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. We used four different variants of this dataset with k=2, 4, 8, and 16 viewing devices, and =10, 10, 10, and 7, respectively. Learning-representations-for-counterfactual-inference - Github the treatment and some contribute to the outcome. 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. 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. stream Limits of estimating heterogeneous treatment effects: Guidelines for Doubly robust policy evaluation and learning. Domain adaptation: Learning bounds and algorithms. \includegraphics[width=0.25]img/nn_pehe. DanielE Ho, Kosuke Imai, Gary King, and ElizabethA Stuart. 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. (ITE) from observational data is an important problem in many domains. (2018), Balancing Neural Network (BNN) Johansson etal. Hw(a? While the underlying idea behind PM is simple and effective, it has, to the best of our knowledge, not yet been explored. We focus on counterfactual questions raised by what areknown asobservational studies. A tag already exists with the provided branch name. Rubin, Donald B. Causal inference using potential outcomes. Learning Decomposed Representation for Counterfactual Inference task. /Length 3974 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. << /Linearized 1 /L 849041 /H [ 2447 819 ] /O 371 /E 54237 /N 78 /T 846567 >> The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. << /Names 366 0 R /OpenAction 483 0 R /Outlines 470 0 R /PageLabels << /Nums [ 0 << /P (0) >> 1 << /P (1) >> 4 << /P (2) >> 5 << /P (3) >> 6 << /P (4) >> 7 << /P (5) >> 11 << /P (6) >> 14 << /P (7) >> 16 << /P (8) >> 20 << /P (9) >> 25 << /P (10) >> 30 << /P (11) >> 32 << /P (12) >> 34 << /P (13) >> 35 << /P (14) >> 39 << /P (15) >> 40 << /P (16) >> 44 << /P (17) >> 49 << /P (18) >> 50 << /P (19) >> 54 << /P (20) >> 57 << /P (21) >> 61 << /P (22) >> 64 << /P (23) >> 65 << /P (24) >> 69 << /P (25) >> 70 << /P (26) >> 77 << /P (27) >> ] >> /PageMode /UseOutlines /Pages 469 0 R /Type /Catalog >>
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