Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates You signed in with another tab or window. 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). We then randomly pick k+1 centroids in topic space, with k centroids zj per viewing device and one control centroid zc. (2011), is that it reduces the variance during training which in turn leads to better expected performance for counterfactual inference (Appendix E). You can register new benchmarks for use from the command line by adding a new entry to the, After downloading IHDP-1000.tar.gz, you must extract the files into the. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We found that NN-PEHE correlates significantly better with the PEHE than MSE (Figure 2). 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. individual treatment effects. In International Conference on Learning Representations. inference which brings together ideas from domain adaptation and representation Our deep learning algorithm significantly outperforms the previous state-of-the-art. Scikit-learn: Machine Learning in Python. This indicates that PM is effective with any low-dimensional balancing score. 167302 within the National Research Program (NRP) 75 Big Data. 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). PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. A literature survey on domain adaptation of statistical classifiers. =0 indicates no assignment bias. Authors: Fredrik D. Johansson. A simple method for estimating interactions between a treatment and a large number of covariates. observed samples X, where each sample consists of p covariates xi with i[0..p1]. Your file of search results citations is now ready. (2011) to estimate p(t|X) for PM on the training set. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). ,E^-"4nhi/dX]/hs9@A$}M\#6soa0YsR/X#+k!"uqAJ3un>e-I~8@f*M9:3qc'RzH ,` (2017). Or, have a go at fixing it yourself the renderer is open source! 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. We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. 3) for News-4/8/16 datasets. The IHDP dataset is biased because the treatment groups had a biased subset of the treated population removed Shalit etal. For the python dependencies, see setup.py. Examples of tree-based methods are Bayesian Additive Regression Trees (BART) Chipman etal. Chengyuan Liu, Leilei Gan, Kun Kuang*, Fei Wu. }Qm4;)v Kang, Joseph DY and Schafer, Joseph L. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. These k-Nearest-Neighbour (kNN) methods Ho etal. "7B}GgRvsp;"DD-NK}si5zU`"98}02 counterfactual inference. The original experiments reported in our paper were run on Intel CPUs. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. stream Our deep learning algorithm significantly outperforms the previous Uri Shalit, FredrikD Johansson, and David Sontag. - Learning-representations-for-counterfactual-inference-. data is confounder identification and balancing. the treatment effect performs better than the state-of-the-art methods on both You can look at the slides here. Jiang, Jing. Generative Adversarial Nets. Run the following scripts to obtain mse.txt, pehe.txt and nn_pehe.txt for use with the. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. 2#w2;0USFJFxp G+=EtA65ztTu=i7}qMX`]vhfw7uD/k^[%_ .r d9mR5GMEe^; :$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. available at this link. This regularises the treatment assignment bias but also introduces data sparsity as not all available samples are leveraged equally for training. By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output. The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework, EMNLP, 2022. 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. On the binary News-2, PM outperformed all other methods in terms of PEHE and ATE. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and 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. PM and the presented experiments are described in detail in our paper. To rectify this problem, we use a nearest neighbour approximation ^NN-PEHE of the ^PEHE metric for the binary Shalit etal. Representation learning: A review and new perspectives. In these situations, methods for estimating causal effects from observational data are of paramount importance. Domain adaptation and sample bias correction theory and algorithm for regression. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The script will print all the command line configurations (1750 in total) you need to run to obtain the experimental results to reproduce the News results. In this sense, PM can be seen as a minibatch sampling strategy Csiba and Richtrik (2018) designed to improve learning for counterfactual inference. << /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>] >> Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. 36 0 obj << 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. PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. 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. Estimation, Treatment Effect Estimation with Unmeasured Confounders in Data Fusion, Learning Disentangled Representations for Counterfactual Regression via Bayesian nonparametric modeling for causal inference. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. Please try again. 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. propose a synergistic learning framework to 1) identify and balance confounders We found that PM better conforms to the desired behavior than PSMPM and PSMMI. Similarly, in economics, a potential application would, for example, be to determine how effective certain job programs would be based on results of past job training programs LaLonde (1986). 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. the treatment and some contribute to the outcome. Tian, Lu, Alizadeh, Ash A, Gentles, Andrew J, and Tibshirani, Robert. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. 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. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. 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. Are you sure you want to create this branch? 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. Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. By modeling the different relations among variables, treatment and outcome, we to install the perfect_match package and the python dependencies. Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. We used four different variants of this dataset with k=2, 4, 8, and 16 viewing devices, and =10, 10, 10, and 7, respectively. 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 (2016) that attempt to find such representations by minimising the discrepancy distance Mansour etal. Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. (2011) before training a TARNET (Appendix G). Copyright 2023 ACM, Inc. Learning representations for counterfactual inference. For IHDP we used exactly the same splits as previously used by Shalit etal. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. 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. CSE, Chalmers University of Technology, Gteborg, Sweden . Causal Multi-task Gaussian Processes (CMGP) Alaa and vander Schaar (2017) apply a multi-task Gaussian Process to ITE estimation. Share on Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. dimensionality. https://archive.ics.uci.edu/ml/datasets/bag+of+words. confounders, ignoring the identification of confounders and non-confounders. PM is easy to use with existing neural network architectures, simple to implement, and does not add any hyperparameters or computational complexity. 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. LauraE. Bothwell, JeremyA. Greene, ScottH. Podolsky, and DavidS. Jones. Domain-adversarial training of neural networks. 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 (2) PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. Your results should match those found in the. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. We repeated experiments on IHDP and News 1000 and 50 times, respectively. (2017) that use different metrics such as the Wasserstein distance. This repo contains the neural network based counterfactual regression implementation for Ad attribution. This is sometimes referred to as bandit feedback (Beygelzimer et al.,2010). We consider the task of answering counterfactual questions such as, Identification and estimation of causal effects of multiple GANITE: Estimation of Individualized Treatment Effects using data. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. (2017). Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. (2016). We therefore suggest to run the commands in parallel using, e.g., a compute cluster. 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. << /Filter /FlateDecode /Length 529 >> Home Browse by Title Proceedings ICML'16 Learning representations for counterfactual inference. Observational data, i.e. 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. Papers With Code is a free resource with all data licensed under. A tag already exists with the provided branch name. (2010); Chipman and McCulloch (2016), Random Forests (RF) Breiman (2001), CF Wager and Athey (2017), GANITE Yoon etal. DanielE Ho, Kosuke Imai, Gary King, ElizabethA Stuart, etal. Inferring the causal effects of interventions is a central pursuit in many important domains, such as healthcare, economics, and public policy. Recursive partitioning for personalization using observational data. Repeat for all evaluated method / benchmark combinations. 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. endobj compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. To perform counterfactual inference, we require knowledge of the underlying. (2017); Alaa and Schaar (2018). 2C&( ??;9xCc@e%yeym? Treatment effect estimation with disentangled latent factors, Adversarial De-confounding in Individualised Treatment Effects Generative Adversarial Nets for inference of Individualised Treatment Effects (GANITE) Yoon etal. 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. (2016), TARNET Shalit etal. Causal inference using potential outcomes: Design, modeling, In TARNET, the jth head network is only trained on samples from treatment tj. Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k 2) and ^mATE (Eq. endobj Pearl, Judea. Repeat for all evaluated method / degree of hidden confounding combinations. 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 literature, this setting is known as the Rubin-Neyman potential outcomes framework Rubin (2005). random forests. >> (2017), Counterfactual Regression Network using the Wasserstein regulariser (CFRNETWass) Shalit etal. 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. F.Pedregosa, G.Varoquaux, A.Gramfort, V.Michel, B.Thirion, O.Grisel, 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. Due to their practical importance, there exists a wide variety of methods for estimating individual treatment effects from observational data. Invited commentary: understanding bias amplification. Counterfactual inference enables one to answer "What if?" Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. A comparison of methods for model selection when estimating Deep counterfactual networks with propensity-dropout. 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. Come up with a framework to train models for factual and counterfactual inference. D.Cournapeau, M.Brucher, M.Perrot, and E.Duchesnay. The shared layers are trained on all samples. We evaluated PM, ablations, baselines, and all relevant state-of-the-art methods: kNN Ho etal. Flexible and expressive models for learning counterfactual representations that generalise to settings with multiple available treatments could potentially facilitate the derivation of valuable insights from observational data in several important domains, such as healthcare, economics and public policy. BART: Bayesian additive regression trees. 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. (2018), Balancing Neural Network (BNN) Johansson etal. /Length 3974 Learning representations for counterfactual inference. Repeat for all evaluated percentages of matched samples. 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. (2018) and multiple treatment settings for model selection. algorithms. M.Blondel, P.Prettenhofer, R.Weiss, V.Dubourg, J.Vanderplas, A.Passos, BayesTree: Bayesian additive regression trees. The advantage of matching on the minibatch level, rather than the dataset level Ho etal. Higher values of indicate a higher expected assignment bias depending on yj. 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.