learning representations for counterfactual inference github

For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). Newman, David. medication?". Copyright 2023 ACM, Inc. Learning representations for counterfactual inference. Cortes, Corinna and Mohri, Mehryar. << /Linearized 1 /L 849041 /H [ 2447 819 ] /O 371 /E 54237 /N 78 /T 846567 >> 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 We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". 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. (2017) subsequently introduced the TARNET architecture to rectify this issue. PM is easy to implement, Free Access. 2023 Neural Causal Models for Counterfactual Identification and Estimation Xia, K., Pan, Y., and Bareinboim, E. (ICLR-23) In Proceedings of the 11th Eleventh International Conference on Learning Representations, Feb 2023 [ pdf , arXiv ] 2022 Causal Transportability for Visual Recognition How do the learning dynamics of minibatch matching compare to dataset-level matching? The role of the propensity score in estimating dose-response https://github.com/vdorie/npci, 2016. (2010); Chipman and McCulloch (2016), Random Forests (RF) Breiman (2001), CF Wager and Athey (2017), GANITE Yoon etal. 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. data is confounder identification and balancing. i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. @E)\a6Hk$$x9B]aV`'iuD Learning Disentangled Representations for CounterFactual Regression 371 0 obj 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 state-of-the-art. Learning representations for counterfactual inference $ ?>jYJW*9Y!WLPD vu{B" j!P?D ; =?5DEE@?8 7@io$. Children that did not receive specialist visits were part of a control group. questions, such as "What would be the outcome if we gave this patient treatment t1?". More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. ITE estimation from observational data is difficult for two reasons: Firstly, we never observe all potential outcomes. MicheleJonsson Funk, Daniel Westreich, Chris Wiesen, Til Strmer, M.Alan E A1 ha!O5 gcO w.M8JP ? Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Perfect Match: A Simple Method for Learning Representations For << /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 >> We therefore conclude that matching on the propensity score or a low-dimensional representation of X and using the TARNET architecture are sensible default configurations, particularly when X is high-dimensional. Interestingly, we found a large improvement over using no matched samples even for relatively small percentages (<40%) of matched samples per batch. He received his M.Sc. A tag already exists with the provided branch name. Our deep learning algorithm significantly outperforms the previous state-of-the-art. Jonas Peters, Dominik Janzing, and Bernhard Schlkopf. 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. dimensionality. 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. PSMMI was overfitting to the treated group. This work was partially funded by the Swiss National Science Foundation (SNSF) project No. 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. We used four different variants of this dataset with k=2, 4, 8, and 16 viewing devices, and =10, 10, 10, and 7, respectively. Deep counterfactual networks with propensity-dropout. 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. In contrast to existing methods, PM is a simple method that can be used to train expressive non-linear neural network models for ITE estimation from observational data in settings with any number of treatments. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. \includegraphics[width=0.25]img/nn_pehe. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. the treatment effect performs better than the state-of-the-art methods on both 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. (2011), is that it reduces the variance during training which in turn leads to better expected performance for counterfactual inference (Appendix E). accumulation of data in fields such as healthcare, education, employment and On IHDP, the PM variants reached the best performance in terms of PEHE, and the second best ATE after CFRNET. After the experiments have concluded, use. endobj Generative Adversarial Nets. Learning representations for counterfactual inference - ICML, 2016. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . Date: February 12, 2020. 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. In this sense, PM can be seen as a minibatch sampling strategy Csiba and Richtrik (2018) designed to improve learning for counterfactual inference. CauseBox | Proceedings of the 30th ACM International Conference on Jinsung Yoon, James Jordon, and Mihaela vander Schaar. Causal effect inference with deep latent-variable models. You signed in with another tab or window. 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. 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. Login. In, Strehl, Alex, Langford, John, Li, Lihong, and Kakade, Sham M. Learning from logged implicit exploration data. {6&m=>9wB$ These k-Nearest-Neighbour (kNN) methods Ho etal. ci0pf=[3@Cm*A,rY`@n 9u_\p=p'h3C'[|kvZMJ:S=9dGC-!43BA RQqr01o:xG ?7>[pM)kC2@p%Np [2023.04.12]: adding a more detailed sd-webui . (2011) before training a TARNET (Appendix G). (ITE) from observational data is an important problem in many domains. A tag already exists with the provided branch name. >> (2007), BART Chipman etal. Federated unsupervised representation learning, FITEE, 2022. Counterfactual reasoning and learning systems: The example of computational advertising. BayesTree: Bayesian additive regression trees. Please try again. ;'/ 2019. In addition to a theoretical justification, we perform an empirical The ATE is not as important as PEHE for models optimised for ITE estimation, but can be a useful indicator of how well an ITE estimator performs at comparing two treatments across the entire population. Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. Repeat for all evaluated percentages of matched samples. (2017). Uri Shalit, FredrikD Johansson, and David Sontag. 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. As a secondary metric, we consider the error ATE in estimating the average treatment effect (ATE) Hill (2011). Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework, EMNLP, 2022. (2) practical algorithm design. The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. learning. 367 0 obj Ben-David, Shai, Blitzer, John, Crammer, Koby, Pereira, Fernando, et al. Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". 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. smartphone, tablet, desktop, television or others Johansson etal. 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. Our empirical results demonstrate that the proposed If you find a rendering bug, file an issue on GitHub. Kang, Joseph DY and Schafer, Joseph L. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. simultaneously 2) estimate the treatment effect in observational studies via inference. stream We perform experiments that demonstrate that PM is robust to a high level of treatment assignment bias and outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmark datasets. "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference." arXiv preprint arXiv:2102.03980, 2021. (2010); Chipman and McCulloch (2016) and Causal Forests (CF) Wager and Athey (2017). The script will print all the command line configurations (13000 in total) you need to run to obtain the experimental results to reproduce the IHDP results. Doubly robust policy evaluation and learning. algorithms. Marginal structural models and causal inference in epidemiology. In. Jennifer L Hill. Improving Unsupervised Vector-Space Thematic Fit Evaluation via Role-Filler Prototype Clustering, Sub-Word Similarity-based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modeling. (2016) that attempt to find such representations by minimising the discrepancy distance Mansour etal. xZY~S[!-"v].8 g9^|94>nKW{[/_=_U{QJUE8>?j+du(KV7>y+ya KO{J4X>+nv^m.U_B;K'pr4])||&ha~2/r5vg9(uT7uo%ztr',a3dZX.6"{3 `1QkP "n3^}. As an Adjunct Lecturer (Lehrbeauftragter) of the Computer Science, and Language Science and Technology departments, he teaches courses on Methods of Mathematical Analysis, Probability Theory, Syntactic Theory, and Computational Linguistics. BART: Bayesian additive regression trees. (2018), Balancing Neural Network (BNN) Johansson etal. Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah. Learning Representations for Counterfactual Inference | DeepAI We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. You signed in with another tab or window. GitHub - OpenTalker/SadTalker: CVPR 2023SadTalkerLearning Realistic 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. A comparison of methods for model selection when estimating Jiang, Jing. Learning Representations for Counterfactual Inference choice without knowing what would be the feedback for other possible choices. Domain adaptation for statistical classifiers. Make sure you have all the requirements listed above. For everything else, email us at [emailprotected]. Upon convergence, under assumption (1) and for. counterfactual inference. 372 0 obj stream 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. Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. treatments under the conditional independence assumption. Matching methods are among the conceptually simplest approaches to estimating ITEs. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. Learning representations for counterfactual inference | Proceedings of Navigate to the directory containing this file. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Symbols correspond to the mean value of, Comparison of several state-of-the-art methods for counterfactual inference on the test set of the News-8 dataset when varying the treatment assignment imbalance, Comparison of methods for counterfactual inference with two and more available treatments on IHDP and News-2/4/8/16. In 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. 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). 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. random forests. (2011). The central role of the propensity score in observational studies for causal effects. Empirical results on synthetic and real-world datasets demonstrate that the proposed method can precisely decompose confounders and achieve a more precise estimation of treatment effect than baselines. We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. %PDF-1.5 Susan Athey, Julie Tibshirani, and Stefan Wager. How does the relative number of matched samples within a minibatch affect performance? RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ 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. We also found that matching on the propensity score was, in almost all cases, not significantly different from matching on X directly when X was low-dimensional, or a low-dimensional representation of X when X was high-dimensional (+ on X). To model that consumers prefer to read certain media items on specific viewing devices, we train a topic model on the whole NY Times corpus and define z(X) as the topic distribution of news item X. Estimation and inference of heterogeneous treatment effects using random forests. 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). /Filter /FlateDecode https://archive.ics.uci.edu/ml/datasets/bag+of+words. Balancing those Observational data, i.e. in Linguistics and Computation from Princeton University. 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. Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. Create a folder to hold the experimental results. Pearl, Judea. We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. (2018) and multiple treatment settings for model selection. stream (2017) that use different metrics such as the Wasserstein distance. Learning-representations-for-counterfactual-inference-MyImplementation. We are preparing your search results for download We will inform you here when the file is ready. (2017), and PD Alaa etal. 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. Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k The advantage of matching on the minibatch level, rather than the dataset level Ho etal. % Counterfactual Inference | Papers With Code data. (3). The topic for this semester at the machine learning seminar was causal inference. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We evaluated PM, ablations, baselines, and all relevant state-of-the-art methods: kNN Ho etal. available at this link. =1(k2)k1i=0i1j=0^PEHE,i,j We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. Domain-adversarial training of neural networks. 167302 within the National Research Program (NRP) 75 Big Data. (2017). endobj functions. 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. 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). PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. Chengyuan Liu, Leilei Gan, Kun Kuang*, Fei Wu. 1) and ATE (Appendix B) for the binary IHDP and News-2 datasets, and the ^mPEHE (Eq. In TARNET, the jth head network is only trained on samples from treatment tj. Fredrik Johansson, Uri Shalit, and David Sontag. 2) and ^mATE (Eq. Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. 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. We found that NN-PEHE correlates significantly better with the PEHE than MSE (Figure 2). 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. Counterfactual inference from observational data always requires further assumptions about the data-generating process Pearl (2009); Peters etal. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Note that we lose the information about the precision in estimating ITE between specific pairs of treatments by averaging over all (k2) pairs. 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 confounders, ignoring the identification of confounders and non-confounders. 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. Morgan, Stephen L and Winship, Christopher. that units with similar covariates xi have similar potential outcomes y. << /Filter /FlateDecode /Length 529 >> GANITE uses a complex architecture with many hyperparameters and sub-models that may be difficult to implement and optimise. PM and the presented experiments are described in detail in our paper. 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 . 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. Analysis of representations for domain adaptation. 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. (2016) and consists of 5000 randomly sampled news articles from the NY Times corpus333https://archive.ics.uci.edu/ml/datasets/bag+of+words. Dorie, Vincent. His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. 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. << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> 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 . 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. endstream Counterfactual inference enables one to answer "What if?" All rights reserved. https://cran.r-project.org/package=BayesTree/, 2016. To perform counterfactual inference, we require knowledge of the underlying. Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates This is likely due to the shared base layers that enable them to efficiently share information across the per-treatment representations in the head networks. ^mPEHE Swaminathan, Adith and Joachims, Thorsten. By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output. Measuring living standards with proxy variables. Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. The ATE measures the average difference in effect across the whole population (Appendix B). We can not guarantee and have not tested compability with Python 3. Observational studies are rising in importance due to the widespread Accessed: 2016-01-30. We outline the Perfect Match (PM) algorithm in Algorithm 1 (complexity analysis and implementation details in Appendix D). 369 0 obj Langford, John, Li, Lihong, and Dudk, Miroslav. We found that PM better conforms to the desired behavior than PSMPM and PSMMI. You can add new benchmarks by implementing the benchmark interface, see e.g. In particular, the source code is designed to be easily extensible with (1) new methods and (2) new benchmark datasets. in Language Science and Technology from Saarland University and his A.B. We perform extensive experiments on semi-synthetic, real-world data in settings with two and more treatments. Causal Multi-task Gaussian Processes (CMGP) Alaa and vander Schaar (2017) apply a multi-task Gaussian Process to ITE estimation. Note that we only evaluate PM, + on X, + MLP, PSM on Jobs. 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). Evaluating the econometric evaluations of training programs with Run the following scripts to obtain mse.txt, pehe.txt and nn_pehe.txt for use with the. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. (2017). We repeated experiments on IHDP and News 1000 and 50 times, respectively. Learning fair representations. A Simple Method for Learning Representations For Counterfactual

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