learning representations for counterfactual inference github

PD, in essence, discounts samples that are far from equal propensity for each treatment during training. (2017) may be used to capture non-linear relationships. His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. Higher values of indicate a higher expected assignment bias depending on yj. Free Access. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. an exact match in the balancing score, for observed factual outcomes. This indicates that PM is effective with any low-dimensional balancing score. The chosen architecture plays a key role in the performance of neural networks when attempting to learn representations for counterfactual inference Shalit etal. We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. Estimating categorical counterfactuals via deep twin networks dont have to squint at a PDF. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. (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. PDF Learning Representations for Counterfactual Inference - arXiv 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. Kun Kuang's Homepage @ Zhejiang University - GitHub Pages https://cran.r-project.org/package=BayesTree/, 2016. (2017). (2017). @E)\a6Hk$$x9B]aV`'iuD In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Your search export query has expired. We found that including more matches indeed consistently reduces the counterfactual error up to 100% of samples matched. Please try again. In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. data. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. inference. To run the IHDP benchmark, you need to download the raw IHDP data folds as used by Johanson et al. Doubly robust policy evaluation and learning. xTn0+H6:iUNAMlm-*P@3,K)WL Learning Decomposed Representation for Counterfactual Inference PMLR, 2016. Learning Disentangled Representations for CounterFactual Regression 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). in Language Science and Technology from Saarland University and his A.B. 167302 within the National Research Program (NRP) 75 "Big Data". 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 . (2011). {6&m=>9wB$ Robins, James M, Hernan, Miguel Angel, and Brumback, Babette. A tag already exists with the provided branch name. 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. in Language Science and Technology from Saarland University and his A.B. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. A kernel two-sample test. 2) and ^mATE (Eq. Counterfactual Inference With Neural Networks, Double Robust Representation Learning for Counterfactual Prediction, Enhancing Counterfactual Classification via Self-Training, Interventional and Counterfactual Inference with Diffusion Models, Continual Causal Inference with Incremental Observational Data, Explaining Deep Learning Models using Causal Inference. 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. In, All Holdings within the ACM Digital Library. 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. You can use pip install . 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. smartphone, tablet, desktop, television or others Johansson etal. >> 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Are you sure you want to create this branch? /Filter /FlateDecode Scikit-learn: Machine Learning in Python. 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. Sign up to our mailing list for occasional updates. CSE, Chalmers University of Technology, Gteborg, Sweden . Counterfactual inference enables one to answer "What if?" Cortes, Corinna and Mohri, Mehryar. 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. As computing systems are more frequently and more actively intervening to improve people's work and daily lives, it is critical to correctly predict and understand the causal effects of these interventions. 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. More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. 0 qA0)#@K5Ih-X8oYH>2{wB2(k`:0P}U)j|B5z.O{?T ;?eKS+9S!9GQAMTl/! To run BART, Causal Forests and to reproduce the figures you need to have R installed. << /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 >> For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). causal effects. However, they are predominantly focused on the most basic setting with exactly two available treatments. D.Cournapeau, M.Brucher, M.Perrot, and E.Duchesnay. 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. }Qm4;)v See https://www.r-project.org/ for installation instructions. Kang, Joseph DY and Schafer, Joseph L. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. To determine the impact of matching fewer than 100% of all samples in a batch, we evaluated PM on News-8 trained with varying percentages of matched samples on the range 0 to 100% in steps of 10% (Figure 4). This is sometimes referred to as bandit feedback (Beygelzimer et al.,2010). Hw(a? endstream 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. For low-dimensional datasets, the covariates X are a good default choice as their use does not require a model of treatment propensity. Rubin, Donald B. Estimating causal effects of treatments in randomized and nonrandomized studies. 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). 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. 3) for News-4/8/16 datasets. 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. 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. Note that we lose the information about the precision in estimating ITE between specific pairs of treatments by averaging over all (k2) pairs. propose a synergistic learning framework to 1) identify and balance confounders Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. BART: Bayesian additive regression trees. Measuring living standards with proxy variables. Please download or close your previous search result export first before starting a new bulk export. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. In medicine, for example, treatment effects are typically estimated via rigorous prospective studies, such as randomised controlled trials (RCTs), and their results are used to regulate the approval of treatments. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. endobj the treatment and some contribute to the outcome. In addition, we trained an ablation of PM where we matched on the covariates X (+ on X) directly, if X was low-dimensional (p<200), and on a 50-dimensional representation of X obtained via principal components analysis (PCA), if X was high-dimensional, instead of on the propensity score. The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework, EMNLP, 2022. https://github.com/vdorie/npci, 2016. Susan Athey, Julie Tibshirani, and Stefan Wager. simultaneously 2) estimate the treatment effect in observational studies via Rosenbaum, Paul R and Rubin, Donald B. We found that PM better conforms to the desired behavior than PSMPM and PSMMI. - Learning-representations-for-counterfactual-inference-. Matching as nonparametric preprocessing for reducing model dependence Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. The advantage of matching on the minibatch level, rather than the dataset level Ho etal. experimental data. The central role of the propensity score in observational studies for causes of both the treatment and the outcome, some variables only contribute to Once you have completed the experiments, you can calculate the summary statistics (mean +- standard deviation) over all the repeated runs using the. Representation Learning. We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. bartMachine: Machine learning with Bayesian additive regression "7B}GgRvsp;"DD-NK}si5zU`"98}02 This work was partially funded by the Swiss National Science Foundation (SNSF) project No. stream 368 0 obj 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. Check if you have access through your login credentials or your institution to get full access on this article. (2017) subsequently introduced the TARNET architecture to rectify this issue. In particular, the source code is designed to be easily extensible with (1) new methods and (2) new benchmark datasets. Learning representations for counterfactual inference - ICML, 2016. In, Strehl, Alex, Langford, John, Li, Lihong, and Kakade, Sham M. Learning from logged implicit exploration data. Inferring the causal effects of interventions is a central pursuit in many important domains, such as healthcare, economics, and public policy. ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. random forests. You can also reproduce the figures in our manuscript by running the R-scripts in. PDF Learning Representations for Counterfactual Inference (2009) between treatment groups, and Counterfactual Regression Networks (CFRNET) Shalit etal. (2010); Chipman and McCulloch (2016), Random Forests (RF) Breiman (2001), CF Wager and Athey (2017), GANITE Yoon etal. 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. endobj non-confounders would generate additional bias for treatment effect estimation. In addition to a theoretical justification, we perform an empirical To ensure that differences between methods of learning counterfactual representations for neural networks are not due to differences in architecture, we based the neural architectures for TARNET, CFRNETWass, PD and PM on the same, previously described extension of the TARNET architecture Shalit etal. The variational fair auto encoder. To rectify this problem, we use a nearest neighbour approximation ^NN-PEHE of the ^PEHE metric for the binary Shalit etal. the treatment effect performs better than the state-of-the-art methods on both 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 perform extensive experiments on semi-synthetic, real-world data in settings with two and more treatments. Your results should match those found in the. Hill, Jennifer L. Bayesian nonparametric modeling for causal inference. You can add new benchmarks by implementing the benchmark interface, see e.g. Doubly robust estimation of causal effects. Approximate nearest neighbors: towards removing the curse of We performed experiments on several real-world and semi-synthetic datasets that showed that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes. 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. 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 then randomly pick k+1 centroids in topic space, with k centroids zj per viewing device and one control centroid zc. BayesTree: Bayesian additive regression trees. F.Pedregosa, G.Varoquaux, A.Gramfort, V.Michel, B.Thirion, O.Grisel, dimensionality. Repeat for all evaluated method / benchmark combinations. 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. Uri Shalit, FredrikD Johansson, and David Sontag. His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. 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 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. % Limits of estimating heterogeneous treatment effects: Guidelines for 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. Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates Repeat for all evaluated method / degree of hidden confounding combinations. PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. 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). Edit social preview. (2017) that use different metrics such as the Wasserstein distance. (2017); Alaa and Schaar (2018). E A1 ha!O5 gcO w.M8JP ? All datasets with the exception of IHDP were split into a training (63%), validation (27%) and test set (10% of samples). Counterfactual inference enables one to answer "What if. algorithms. Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Causal effect inference with deep latent-variable models. 372 0 obj Learning representations for counterfactual inference | Proceedings of Learning-representations-for-counterfactual-inference - Github (2000); Louizos etal. Domain adaptation for statistical classifiers. We used four different variants of this dataset with k=2, 4, 8, and 16 viewing devices, and =10, 10, 10, and 7, respectively. In. 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. ^mPEHE The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. How does the relative number of matched samples within a minibatch affect performance? Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. Marginal structural models and causal inference in epidemiology. cq?g A Simple Method for Learning Representations For Counterfactual The ATE measures the average difference in effect across the whole population (Appendix B). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. state-of-the-art. 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). (2017), Counterfactual Regression Network using the Wasserstein regulariser (CFRNETWass) Shalit etal. Learning Representations for Counterfactual Inference choice without knowing what would be the feedback for other possible choices. Rubin, Donald B. Causal inference using potential outcomes. that units with similar covariates xi have similar potential outcomes y. << /Linearized 1 /L 849041 /H [ 2447 819 ] /O 371 /E 54237 /N 78 /T 846567 >> 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. Learning representations for counterfactual inference. Note: Create a results directory before executing Run.py. 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. (2017). (2018) and multiple treatment settings for model selection. (2018) address ITE estimation using counterfactual and ITE generators. Our experiments aimed to answer the following questions: What is the comparative performance of PM in inferring counterfactual outcomes in the binary and multiple treatment setting compared to existing state-of-the-art methods? Run the command line configurations from the previous step in a compute environment of your choice. ?" questions, such as "What would be the outcome if we gave this patient treatment t 1 ?". stream Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". Copyright 2023 ACM, Inc. Learning representations for counterfactual inference. Interestingly, we found a large improvement over using no matched samples even for relatively small percentages (<40%) of matched samples per batch. Accessed: 2016-01-30. stream in parametric causal inference. Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. Share on << /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>] >> Home Browse by Title Proceedings ICML'16 Learning representations for counterfactual inference. The source code for this work is available at https://github.com/d909b/perfect_match. This regularises the treatment assignment bias but also introduces data sparsity as not all available samples are leveraged equally for training. decisions. 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. data is confounder identification and balancing. 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. You signed in with another tab or window. %PDF-1.5 ^mATE stream available at this link. << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> The topic for this semester at the machine learning seminar was causal inference. Use of the logistic model in retrospective studies. 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. Examples of tree-based methods are Bayesian Additive Regression Trees (BART) Chipman etal. 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. 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. (2017), and PD Alaa etal. We extended the original dataset specification in Johansson etal. The IHDP dataset is biased because the treatment groups had a biased subset of the treated population removed Shalit etal. Counterfactual reasoning and learning systems: The example of computational advertising. We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. In this sense, PM can be seen as a minibatch sampling strategy Csiba and Richtrik (2018) designed to improve learning for counterfactual inference. Estimation, Treatment Effect Estimation with Unmeasured Confounders in Data Fusion, Learning Disentangled Representations for Counterfactual Regression via Balancing those Tree-based methods train many weak learners to build expressive ensemble models. Bayesian inference of individualized treatment effects using (2007). 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 . confounders, ignoring the identification of confounders and non-confounders. endobj 373 0 obj Date: February 12, 2020. stream Besides accounting for the treatment assignment bias, the other major issue in learning for counterfactual inference from observational data is that, given multiple models, it is not trivial to decide which one to select. Learning representations for counterfactual inference. A comparison of methods for model selection when estimating 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 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. 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.

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