compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. 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. Login. After the experiments have concluded, use. 0 qA0)#@K5Ih-X8oYH>2{wB2(k`:0P}U)j|B5z.O{?T ;?eKS+9S!9GQAMTl/! To perform counterfactual inference, we require knowledge of the underlying. Estimating individual treatment effects111The ITE is sometimes also referred to as the conditional average treatment effect (CATE). In International Conference on Learning Representations. task. 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 His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. The original experiments reported in our paper were run on Intel CPUs. Jennifer L Hill. (2017) (Appendix H) to the multiple treatment setting. These k-Nearest-Neighbour (kNN) methods Ho etal. d909b/perfect_match - Github PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. 371 0 obj Then, I will share the educational objectives for students of data science inspired by my research, and how, with interactive and innovative teaching, I have trained and will continue to train students to be successful in their scientific pursuits. The optimisation of CMGPs involves a matrix inversion of O(n3) complexity that limits their scalability. Both PEHE and ATE can be trivially extended to multiple treatments by considering the average PEHE and ATE between every possible pair of treatments. random forests. accumulation of data in fields such as healthcare, education, employment and 2019. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. We report the mean value. 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). 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. We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. 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. While the underlying idea behind PM is simple and effective, it has, to the best of our knowledge, not yet been explored. PDF Learning Representations for Counterfactual Inference NPCI: Non-parametrics for causal inference. arXiv as responsive web pages so you https://archive.ics.uci.edu/ml/datasets/bag+of+words. Measuring living standards with proxy variables. (2018) address ITE estimation using counterfactual and ITE generators. However, in many settings of interest, randomised experiments are too expensive or time-consuming to execute, or not possible for ethical reasons Carpenter (2014); Bothwell etal. 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 simple method for estimating interactions between a treatment and a large number of covariates. 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. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. Since the original TARNET was limited to the binary treatment setting, we extended the TARNET architecture to the multiple treatment setting (Figure 1). 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. Speaker: Clayton Greenberg, Ph.D. Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. 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. In these situations, methods for estimating causal effects from observational data are of paramount importance. HughA Chipman, EdwardI George, RobertE McCulloch, etal. 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. trees. 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). 167302 within the National Research Program (NRP) 75 Big Data. Accessed: 2016-01-30. We use cookies to ensure that we give you the best experience on our website. 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 . in parametric causal inference. (2017). Bigger and faster computation creates such an opportunity to answer what previously seemed to be unanswerable research questions, but also can be rendered meaningless if the structure of the data is not sufficiently understood. 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 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. The set of available treatments can contain two or more treatments. Learning Representations for Counterfactual Inference 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. (2007). Examples of representation-balancing methods are Balancing Neural Networks Johansson etal. zz !~A|66}$EPp("i n $* The propensity score with continuous treatments. Daume III, Hal and Marcu, Daniel. 2011. ]|2jZ;lU.t`' Domain adaptation and sample bias correction theory and algorithm for regression. xZY~S[!-"v].8 g9^|94>nKW{[/_=_U{QJUE8>?j+du(KV7>y+ya available at this link. BayesTree: Bayesian additive regression trees. "Would this patient have lower blood sugar had she received a different We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. We found that NN-PEHE correlates significantly better with the PEHE than MSE (Figure 2). (2016) that attempt to find such representations by minimising the discrepancy distance Mansour etal. Shalit etal. Are you sure you want to create this branch? causes of both the treatment and the outcome, some variables only contribute to The primary metric that we optimise for when training models to estimate ITE is the PEHE Hill (2011). We propose a new algorithmic framework for counterfactual By modeling the different relations among variables, treatment and outcome, we Candidate at the Saarland University Graduate School of Computer Science, where he is advised by Dietrich Klakow. We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. (2016), TARNET Shalit etal. an exact match in the balancing score, for observed factual outcomes. We used four different variants of this dataset with k=2, 4, 8, and 16 viewing devices, and =10, 10, 10, and 7, respectively. However, they are predominantly focused on the most basic setting with exactly two available treatments. Share on Jingyu He, Saar Yalov, and P Richard Hahn. << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> Your search export query has expired. LauraE. Bothwell, JeremyA. Greene, ScottH. Podolsky, and DavidS. Jones. Doubly robust policy evaluation and learning. ?" questions, such as "What would be the outcome if we gave this patient treatment t 1 ?". /Length 3974 (2016). dimensionality. You can add new benchmarks by implementing the benchmark interface, see e.g. 367 0 obj 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). Newman, David. 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). (2000); Louizos etal. A kernel two-sample test. << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> 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. Learning Decomposed Representation for Counterfactual Inference Gretton, Arthur, Borgwardt, Karsten M., Rasch, Malte J., Schlkopf, Bernhard, and Smola, Alexander. Learning representations for counterfactual inference. xcbdg`b`8 $S&`6Ah :H) @DH301?e`%x]0 > ; See https://www.r-project.org/ for installation instructions. Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework, EMNLP, 2022. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. 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. Louizos, Christos, Swersky, Kevin, Li, Yujia, Welling, Max, and Zemel, Richard. Learning Disentangled Representations for CounterFactual Regression GANITE: Estimation of Individualized Treatment Effects using 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. Recursive partitioning for personalization using observational data. Repeat for all evaluated method / benchmark combinations. 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. inference which brings together ideas from domain adaptation and representation =1(k2)k1i=0i1j=0^ATE,i,jt \includegraphics[width=0.25]img/nn_pehe. (3). Authors: Fredrik D. Johansson. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. 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. 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. Simulated data has been used as the input to PrepareData.py which would be followed by the execution of Run.py. [Takeuchi et al., 2021] Takeuchi, Koh, et al. 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 Repeat for all evaluated method / degree of hidden confounding combinations. counterfactual inference. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. Prentice, Ross. 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. 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). We consider a setting in which we are given N i.i.d. 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. This regularises the treatment assignment bias but also introduces data sparsity as not all available samples are leveraged equally for training. << /Filter /FlateDecode /Length 529 >> Article . van der Laan, Mark J and Petersen, Maya L. Causal effect models for realistic individualized treatment and intention to treat rules. Run the command line configurations from the previous step in a compute environment of your choice. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. In, Strehl, Alex, Langford, John, Li, Lihong, and Kakade, Sham M. Learning from logged implicit exploration data. << /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>] >> Balancing those Treatment effect estimation with disentangled latent factors, Adversarial De-confounding in Individualised Treatment Effects 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. Our empirical results demonstrate that the proposed 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. 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: . 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. ecology. $ @?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 Free Access. 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. multi-task gaussian processes. Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. The chosen architecture plays a key role in the performance of neural networks when attempting to learn representations for counterfactual inference Shalit etal. For IHDP we used exactly the same splits as previously used by Shalit etal. (2017). 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. 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. confounders, ignoring the identification of confounders and non-confounders. Cortes, Corinna and Mohri, Mehryar. A literature survey on domain adaptation of statistical classifiers. endstream Learning disentangled representations for counterfactual regression. Causal Multi-task Gaussian Processes (CMGP) Alaa and vander Schaar (2017) apply a multi-task Gaussian Process to ITE estimation. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Learning representations for counterfactual inference - ICML, 2016. Batch learning from logged bandit feedback through counterfactual risk minimization. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and The IHDP dataset is biased because the treatment groups had a biased subset of the treated population removed Shalit etal. 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. Matching as nonparametric preprocessing for reducing model dependence "7B}GgRvsp;"DD-NK}si5zU`"98}02 Although deep learning models have been successfully applied to a variet MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population, Perfect Match: A Simple Method for Learning Representations For Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Learning Decomposed Representation for Counterfactual Inference (2017) claimed that the nave approach of appending the treatment index tj may perform poorly if X is high-dimensional, because the influence of tj on the hidden layers may be lost during training. %PDF-1.5 Edit social preview. (2017) may be used to capture non-linear relationships. Marginal structural models and causal inference in epidemiology. PM may be used for settings with any amount of treatments, is compatible with any existing neural network architecture, simple to implement, and does not introduce any additional hyperparameters or computational complexity. We develop performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual treatment effects in the setting with multiple available treatments. Jiang, Jing. Tree-based methods train many weak learners to build expressive ensemble models. Please try again. Our deep learning algorithm significantly outperforms the previous non-confounders would generate additional bias for treatment effect estimation. Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. Share on. Due to their practical importance, there exists a wide variety of methods for estimating individual treatment effects from observational data.
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