Run the command line configurations from the previous step in a compute environment of your choice. We calculated the PEHE (Eq. Bio: Clayton Greenberg is a Ph.D. For the python dependencies, see setup.py. We evaluated PM, ablations, baselines, and all relevant state-of-the-art methods: kNN Ho etal. MatchIt: nonparametric preprocessing for parametric causal Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and Max Welling. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. 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. (2007). For low-dimensional datasets, the covariates X are a good default choice as their use does not require a model of treatment propensity. The topic for this semester at the machine learning seminar was causal inference. Estimation, Treatment Effect Estimation with Unmeasured Confounders in Data Fusion, Learning Disentangled Representations for Counterfactual Regression via By modeling the different relations among variables, treatment and outcome, we 368 0 obj 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. "Would this patient have lower blood sugar had she received a different 367 0 obj In addition to a theoretical justification, we perform an empirical }Qm4;)v (2016). https://dl.acm.org/doi/abs/10.5555/3045390.3045708. To compute the PEHE, we measure the mean squared error between the true difference in effect y1(n)y0(n), drawn from the noiseless underlying outcome distributions 1 and 0, and the predicted difference in effect ^y1(n)^y0(n) indexed by n over N samples: When the underlying noiseless distributions j are not known, the true difference in effect y1(n)y0(n) can be estimated using the noisy ground truth outcomes yi (Appendix A). Your search export query has expired. PM and the presented experiments are described in detail in our paper. simultaneously 2) estimate the treatment effect in observational studies via Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 167302 within the National Research Program (NRP) 75 Big Data. 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 Invited commentary: understanding bias amplification. We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k an exact match in the balancing score, for observed factual outcomes. Learning Decomposed Representation for Counterfactual Inference BayesTree: Bayesian additive regression trees. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. 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. Langford, John, Li, Lihong, and Dudk, Miroslav. the treatment effect performs better than the state-of-the-art methods on both These k-Nearest-Neighbour (kNN) methods Ho etal. data is confounder identification and balancing. (2017); Alaa and Schaar (2018). 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. 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. Evaluating the econometric evaluations of training programs with All rights reserved. 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). Rosenbaum, Paul R and Rubin, Donald B. Our deep learning algorithm significantly outperforms the previous state-of-the-art. 371 0 obj 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. Causal inference using potential outcomes: Design, modeling, We consider a setting in which we are given N i.i.d. ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. The distribution of samples may therefore differ significantly between the treated group and the overall population. 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. However, one can inspect the pair-wise PEHE to obtain the whole picture. data that has not been collected in a randomised experiment, on the other hand, is often readily available in large quantities. We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. Learning representations for counterfactual inference | Proceedings of comparison with previous approaches to causal inference from observational Jonas Peters, Dominik Janzing, and Bernhard Schlkopf. %PDF-1.5 In. << /Filter /FlateDecode /Length 529 >> You can add new benchmarks by implementing the benchmark interface, see e.g. 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. (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. In. PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. DanielE Ho, Kosuke Imai, Gary King, and ElizabethA Stuart. 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. /Length 3974 We found that PM better conforms to the desired behavior than PSMPM and PSMMI. This work was partially funded by the Swiss National Science Foundation (SNSF) project No. We consider the task of answering counterfactual questions such as, Comparison of the learning dynamics during training (normalised training epochs; from start = 0 to end = 100 of training, x-axis) of several matching-based methods on the validation set of News-8. Counterfactual Inference | Papers With Code stream Chipman, Hugh and McCulloch, Robert. (2011) before training a TARNET (Appendix G). 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. 369 0 obj Representation Learning: What Is It and How Do You Teach It? << /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>] >> This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Tian, Lu, Alizadeh, Ash A, Gentles, Andrew J, and Tibshirani, Robert. general, not all the observed variables are confounders which are the common Want to hear about new tools we're making? All datasets with the exception of IHDP were split into a training (63%), validation (27%) and test set (10% of samples). 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. (2010); Chipman and McCulloch (2016) and Causal Forests (CF) Wager and Athey (2017). After the experiments have concluded, use. (2011), is that it reduces the variance during training which in turn leads to better expected performance for counterfactual inference (Appendix E). The outcomes were simulated using the NPCI package from Dorie (2016)222We used the same simulated outcomes as Shalit etal. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. The original experiments reported in our paper were run on Intel CPUs. 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). In this talk I presented and discussed a paper which aimed at developping a framework for factual and counterfactual inference. Mutual Information Minimization, The Effect of Medicaid Expansion on Non-Elderly Adult Uninsurance Rates propose a synergistic learning framework to 1) identify and balance confounders stream non-confounders would generate additional bias for treatment effect estimation. task. PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. Contributions. 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. 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 repeated experiments on IHDP and News 1000 and 50 times, respectively. Learning Disentangled Representations for CounterFactual Regression (2017). (2011) to estimate p(t|X) for PM on the training set. 2C&( ??;9xCc@e%yeym? 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. 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. ITE estimation from observational data is difficult for two reasons: Firstly, we never observe all potential outcomes. in Language Science and Technology from Saarland University and his A.B. xZY~S[!-"v].8 g9^|94>nKW{[/_=_U{QJUE8>?j+du(KV7>y+ya Treatment effect estimation with disentangled latent factors, Adversarial De-confounding in Individualised Treatment Effects Limits of estimating heterogeneous treatment effects: Guidelines for The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. In, All Holdings within the ACM Digital Library. 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. 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. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Domain adaptation for statistical classifiers. We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. You can also reproduce the figures in our manuscript by running the R-scripts in. A simple method for estimating interactions between a treatment and a large number of covariates. % [HJ)mD:K`G?/BPWw(a&ggl }[OvP ps@]TZP?x ;_[YN^0'5 Bayesian inference of individualized treatment effects using PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. Article . 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. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. Once you have completed the experiments, you can calculate the summary statistics (mean +- standard deviation) over all the repeated runs using the. Note that we ran several thousand experiments which can take a while if evaluated sequentially. The set of available treatments can contain two or more treatments. Pearl, Judea. Authors: Fredrik D. Johansson. However, current methods for training neural networks for counterfactual . Counterfactual inference enables one to answer "What if?" 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. In these situations, methods for estimating causal effects from observational data are of paramount importance. (2016) that attempt to find such representations by minimising the discrepancy distance Mansour etal. 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. Estimating categorical counterfactuals via deep twin networks 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. Or, have a go at fixing it yourself the renderer is open source! "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference." arXiv preprint arXiv:2102.03980, 2021. to install the perfect_match package and the python dependencies. 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. On IHDP, the PM variants reached the best performance in terms of PEHE, and the second best ATE after CFRNET. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Recursive partitioning for personalization using observational data. Propensity Dropout (PD) Alaa etal. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. Are you sure you want to create this branch? Counterfactual inference enables one to answer "What if. On the News-4/8/16 datasets with more than two treatments, PM consistently outperformed all other methods - in some cases by a large margin - on both metrics with the exception of the News-4 dataset, where PM came second to PD. << /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 >> (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). 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. % In International Conference on Learning Representations. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. (2009) between treatment groups, and Counterfactual Regression Networks (CFRNET) Shalit etal. A tag already exists with the provided branch name. 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. 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. Children that did not receive specialist visits were part of a control group. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). He received his M.Sc. Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. (3). We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. endobj Swaminathan, Adith and Joachims, Thorsten. We outline the Perfect Match (PM) algorithm in Algorithm 1 (complexity analysis and implementation details in Appendix D). Morgan, Stephen L and Winship, Christopher. Wager, Stefan and Athey, Susan. Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. 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. AhmedM Alaa, Michael Weisz, and Mihaela vander Schaar. BART: Bayesian additive regression trees. In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 167302 within the National Research Program (NRP) 75 "Big Data". This work was partially funded by the Swiss National Science Foundation (SNSF) project No. Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . Propensity Score Matching (PSM) Rosenbaum and Rubin (1983) addresses this issue by matching on the scalar probability p(t|X) of t given the covariates X. Cortes, Corinna and Mohri, Mehryar. ^mATE Dorie, Vincent. 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. Counterfactual inference enables one to answer "What if?" Prentice, Ross. 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. The central role of the propensity score in observational studies for causal effects. Mansour, Yishay, Mohri, Mehryar, and Rostamizadeh, Afshin. Jiang, Jing. Learning Representations for Counterfactual Inference Repeat for all evaluated method / benchmark combinations. Causal effect inference with deep latent-variable models. task. Domain adaptation: Learning bounds and algorithms. Free Access. 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. How well does PM cope with an increasing treatment assignment bias in the observed data? 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. One fundamental problem in the learning treatment effect from observational 2011. Learning representations for counterfactual inference compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. PDF Learning Representations for Counterfactual Inference - arXiv 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. 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. realized confounder balancing by treating all observed variables as Learning-representations-for-counterfactual-inference-MyImplementation. by learning decomposed representation of confounders and non-confounders, and state-of-the-art. This setup comes up in diverse areas, for example off-policy evalu-ation in reinforcement learning (Sutton & Barto,1998), To run the IHDP benchmark, you need to download the raw IHDP data folds as used by Johanson et al. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Learning representations for counterfactual inference. stream Dudk, Miroslav, Langford, John, and Li, Lihong. As a secondary metric, we consider the error ATE in estimating the average treatment effect (ATE) Hill (2011). cq?g PM effectively controls for biased assignment of treatments in observational data by augmenting every sample within a minibatch with its closest matches by propensity score from the other treatments. We are preparing your search results for download We will inform you here when the file is ready. Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah. In general, not all the observed pre-treatment variables are confounders that refer to the common causes of the treatment and the outcome, some variables only contribute to the treatment and some only contribute to the outcome. Bag of words data set. We therefore suggest to run the commands in parallel using, e.g., a compute cluster. bartMachine: Machine learning with Bayesian additive regression Bang, Heejung and Robins, James M. Doubly robust estimation in missing data and causal inference models.