369 0 obj Use of the logistic model in retrospective studies. Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". This repo contains the neural network based counterfactual regression implementation for Ad attribution. A tag already exists with the provided branch name. Note that we only evaluate PM, + on X, + MLP, PSM on Jobs. 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. In particular, the source code is designed to be easily extensible with (1) new methods and (2) new benchmark datasets. 367 0 obj 3) for News-4/8/16 datasets. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. Note the installation of rpy2 will fail if you do not have a working R installation on your system (see above). (2011). We perform extensive experiments on semi-synthetic, real-world data in settings with two and more treatments. 2C&( ??;9xCc@e%yeym? 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. Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Bayesian inference of individualized treatment effects using RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ 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. 2011. (2016). BayesTree: Bayesian additive regression trees. How well does PM cope with an increasing treatment assignment bias in the observed data? Repeat for all evaluated method / degree of hidden confounding combinations. How do the learning dynamics of minibatch matching compare to dataset-level matching? Causal inference using potential outcomes: Design, modeling, Want to hear about new tools we're making? Sign up to our mailing list for occasional updates. xTn0+H6:iUNAMlm-*P@3,K)WL 167302 within the National Research Program (NRP) 75 Big Data. The original experiments reported in our paper were run on Intel CPUs. Note that we ran several thousand experiments which can take a while if evaluated sequentially. Uri Shalit, FredrikD Johansson, and David Sontag. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. In. 4. (2016) to enable the simulation of arbitrary numbers of viewing devices. endobj You can add new benchmarks by implementing the benchmark interface, see e.g. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. ecology. [Takeuchi et al., 2021] Takeuchi, Koh, et al. dont have to squint at a PDF. smartphone, tablet, desktop, television or others Johansson etal. Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. GitHub - OpenTalker/SadTalker: CVPR 2023SadTalkerLearning Realistic Your search export query has expired. decisions. Note that we lose the information about the precision in estimating ITE between specific pairs of treatments by averaging over all (k2) pairs. Causal effect inference with deep latent-variable models. Gani, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, Franois, Marchand, Mario, and Lempitsky, Victor. in Language Science and Technology from Saarland University and his A.B. 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. PMLR, 1130--1138. 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. (2000); Louizos etal. Repeat for all evaluated percentages of matched samples. If you find a rendering bug, file an issue on GitHub. Several new mode, eg, still mode, reference mode, resize mode are online for better and custom applications.. Happy to see more community demos at bilibili, Youtube and twitter #sadtalker.. Changelog (Previous changelog can be founded here) [2023.04.15]: Adding automatic1111 colab by @camenduru, thanks for this awesome colab: . 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. Bag of words data set. stream questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". in parametric causal inference. The script will print all the command line configurations (450 in total) you need to run to obtain the experimental results to reproduce the News results. Jennifer L Hill. << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> accumulation of data in fields such as healthcare, education, employment and Evaluating the econometric evaluations of training programs with https://archive.ics.uci.edu/ml/datasets/bag+of+words. 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. We report the mean value. 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. Repeat for all evaluated methods / levels of kappa combinations. 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. "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference." arXiv preprint arXiv:2102.03980, 2021. He received his M.Sc. 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). }Qm4;)v 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. (2010); Chipman and McCulloch (2016), Random Forests (RF) Breiman (2001), CF Wager and Athey (2017), GANITE Yoon etal. MatchIt: nonparametric preprocessing for parametric causal His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. Share on. (2017); Schuler etal. KO{J4X>+nv^m.U_B;K'pr4])||&ha~2/r5vg9(uT7uo%ztr',a3dZX.6"{3 `1QkP "n3^}. Higher values of indicate a higher expected assignment bias depending on yj. Estimating categorical counterfactuals via deep twin networks 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. method can precisely identify and balance confounders, while the estimation of realized confounder balancing by treating all observed variables as (2017), and PD Alaa etal. Prentice, Ross. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. 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. In. task. After the experiments have concluded, use. You signed in with another tab or window. These k-Nearest-Neighbour (kNN) methods Ho etal. 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. "Learning representations for counterfactual inference." International conference on machine learning. 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 found that PM better conforms to the desired behavior than PSMPM and PSMMI. Dorie, Vincent. Propensity Dropout (PD) Alaa etal. (2018) address ITE estimation using counterfactual and ITE generators. Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. We extended the original dataset specification in Johansson etal. To run the IHDP benchmark, you need to download the raw IHDP data folds as used by Johanson et al. https://github.com/vdorie/npci, 2016. 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. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. Robins, James M, Hernan, Miguel Angel, and Brumback, Babette. stream For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. 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. Estimation and inference of heterogeneous treatment effects using random forests. 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. (2) You can look at the slides here. 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. Recursive partitioning for personalization using observational data. Quick introduction to CounterFactual Regression (CFR) (2007), BART Chipman etal. Fredrik Johansson, Uri Shalit, and David Sontag. The topic for this semester at the machine learning seminar was causal inference. In this sense, PM can be seen as a minibatch sampling strategy Csiba and Richtrik (2018) designed to improve learning for counterfactual inference. We then defined the unscaled potential outcomes yj=~yj[D(z(X),zj)+D(z(X),zc)] as the ideal potential outcomes ~yj weighted by the sum of distances to centroids zj and the control centroid zc using the Euclidean distance as distance D. We assigned the observed treatment t using t|xBern(softmax(yj)) with a treatment assignment bias coefficient , and the true potential outcome yj=Cyj as the unscaled potential outcomes yj scaled by a coefficient C=50. ci0pf=[3@Cm*A,rY`@n 9u_\p=p'h3C'[|kvZMJ:S=9dGC-!43BA RQqr01o:xG ?7>[pM)kC2@p%Np Tian, Lu, Alizadeh, Ash A, Gentles, Andrew J, and Tibshirani, Robert. Matching methods are among the conceptually simplest approaches to estimating ITEs. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Treatment effect estimation with disentangled latent factors, Adversarial De-confounding in Individualised Treatment Effects [HJ)mD:K`G?/BPWw(a&ggl }[OvP ps@]TZP?x ;_[YN^0'5 On IHDP, the PM variants reached the best performance in terms of PEHE, and the second best ATE after CFRNET. Cortes, Corinna and Mohri, Mehryar. This indicates that PM is effective with any low-dimensional balancing score. PDF Learning Representations for Counterfactual Inference - arXiv Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates 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). We therefore suggest to run the commands in parallel using, e.g., a compute cluster. Doubly robust estimation of causal effects. data. rk*>&TaYh%gc,(| DiJIRR?ZzfT"Zv$]}-P+"{Z4zVSNXs$kHyS$z>q*BHA"6#d.wtt3@V^SL+xm=,mh2\'UHum8Nb5gI >VtU i-zkAz~b6;]OB9:>g#{(XYW>idhKt Accessed: 2016-01-30. r/WI7FW*^e~gNdk}4]iE3it0W}]%Cw5"$HhKxYlR&{Y_{R~MkE}R0#~8$LVDt*EG_Q hMZk5jCNm1Y%i8vb3 E8&R/g2}h%X7.jR*yqmEi|[$/?XBo{{kSjWIlW 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. Make sure you have all the requirements listed above. (2007). We did so by using k head networks, one for each treatment over a set of shared base layers, each with L layers. 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. https://dl.acm.org/doi/abs/10.5555/3045390.3045708. In, All Holdings within the ACM Digital Library. ITE estimation from observational data is difficult for two reasons: Firstly, we never observe all potential outcomes. Contributions. Generative Adversarial Nets. In addition to a theoretical justification, we perform an empirical (2011) before training a TARNET (Appendix G). Your file of search results citations is now ready. Jiang, Jing. /Filter /FlateDecode << /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>] >> << /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 >> (2010); Chipman and McCulloch (2016) and Causal Forests (CF) Wager and Athey (2017). AhmedM Alaa, Michael Weisz, and Mihaela vander Schaar. We consider a setting in which we are given N i.i.d. PM and the presented experiments are described in detail in our paper. (2011) to estimate p(t|X) for PM on the training set. Please download or close your previous search result export first before starting a new bulk export. Navigate to the directory containing this file. PSMMI was overfitting to the treated group. endobj Learning Representations for Counterfactual Inference choice without knowing what would be the feedback for other possible choices. 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). /Length 3974 Learning-representations-for-counterfactual-inference-MyImplementation. The root problem is that we do not have direct access to the true error in estimating counterfactual outcomes, only the error in estimating the observed factual outcomes. 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. We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. Bayesian nonparametric modeling for causal inference. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. All datasets with the exception of IHDP were split into a training (63%), validation (27%) and test set (10% of samples). Candidate at the Saarland University Graduate School of Computer Science, where he is advised by Dietrich Klakow. confounders, ignoring the identification of confounders and non-confounders. 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 endobj (2017) adjusts the regularisation for each sample during training depending on its treatment propensity. state-of-the-art. We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. 372 0 obj We evaluated the counterfactual inference performance of the listed models in settings with two or more available treatments (Table 1, ATEs in Appendix Table S3). stream Domain-adversarial training of neural networks. For IHDP we used exactly the same splits as previously used by Shalit etal. 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. 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 . We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. arXiv Vanity renders academic papers from Observational data, i.e. algorithms. 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. We use cookies to ensure that we give you the best experience on our website. (2017) is another method using balancing scores that has been proposed to dynamically adjust the dropout regularisation strength for each observed sample depending on its treatment propensity. counterfactual inference. 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. i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Please try again. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. This work contains the following contributions: We introduce Perfect Match (PM), a simple methodology based on minibatch matching for learning neural representations for counterfactual inference in settings with any number of treatments. Since we performed one of the most comprehensive evaluations to date with four different datasets with varying characteristics, this repository may serve as a benchmark suite for developing your own methods for estimating causal effects using machine learning methods. By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output. =0 indicates no assignment bias. =1(k2)k1i=0i1j=0^ATE,i,jt ,E^-"4nhi/dX]/hs9@A$}M\#6soa0YsR/X#+k!"uqAJ3un>e-I~8@f*M9:3qc'RzH ,` DanielE Ho, Kosuke Imai, Gary King, ElizabethA Stuart, etal. In medicine, for example, we would be interested in using data of people that have been treated in the past to predict what medications would lead to better outcomes for new patients Shalit etal. To address these problems, we introduce Perfect Match (PM), a simple method for training neural networks for counterfactual inference that extends to any number of treatments. Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. PM is easy to use with existing neural network architectures, simple to implement, and does not add any hyperparameters or computational complexity. 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). CRM, also known as batch learning from bandit feedback, optimizes the policy model by maximizing its reward estimated with a counterfactual risk estimator (Dudk, Langford, and Li 2011 .
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