Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. You signed in with another tab or window. Learning representations for counterfactual inference 372 0 obj Bayesian nonparametric modeling for causal inference. NPCI: Non-parametrics for causal inference, 2016. Accessed: 2016-01-30. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. For the python dependencies, see setup.py. The experiments show that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes from observational data. Identification and estimation of causal effects of multiple endstream 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. Treatment effect estimation with disentangled latent factors, Adversarial De-confounding in Individualised Treatment Effects We use cookies to ensure that we give you the best experience on our website. Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. 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. 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. Generative Adversarial Nets. functions. Quick introduction to CounterFactual Regression (CFR) Uri Shalit, FredrikD Johansson, and David Sontag. In The 22nd International Conference on Artificial Intelligence and Statistics. Counterfactual Inference | Papers With Code We performed experiments on two real-world and semi-synthetic datasets with binary and multiple treatments in order to gain a better understanding of the empirical properties of PM. For each sample, the potential outcomes are represented as a vector Y with k entries yj where each entry corresponds to the outcome when applying one treatment tj out of the set of k available treatments T={t0,,tk1} with j[0..k1]. The advantage of matching on the minibatch level, rather than the dataset level Ho etal. Learning representations for counterfactual inference. zz !~A|66}$EPp("i n $* 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. Susan Athey, Julie Tibshirani, and Stefan Wager. A comparison of methods for model selection when estimating /Length 3974 Domain adaptation: Learning bounds and algorithms. 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. Bang, Heejung and Robins, James M. Doubly robust estimation in missing data and causal inference models. Your file of search results citations is now ready. Repeat for all evaluated method / benchmark combinations. endobj We are preparing your search results for download We will inform you here when the file is ready. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. (2011). PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. (2017). For IHDP we used exactly the same splits as previously used by Shalit etal. (2017). Counterfactual inference enables one to answer "What if?" 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. endobj << /Filter /FlateDecode /Length 529 >> stream Make sure you have all the requirements listed above. confounders, ignoring the identification of confounders and non-confounders. 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. Please try again. 1) and ATE (Appendix B) for the binary IHDP and News-2 datasets, and the ^mPEHE (Eq. Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. 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 propose a synergistic learning framework to 1) identify and balance confounders Robins, James M, Hernan, Miguel Angel, and Brumback, Babette. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. However, one can inspect the pair-wise PEHE to obtain the whole picture. endobj Bag of words data set. data. 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). Free Access. endobj 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. 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. RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ The optimisation of CMGPs involves a matrix inversion of O(n3) complexity that limits their scalability. learning. (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). This setup comes up in diverse areas, for example off-policy evalu-ation in reinforcement learning (Sutton & Barto,1998), questions, such as "What would be the outcome if we gave this patient treatment t1?". These k-Nearest-Neighbour (kNN) methods Ho etal. (2007), BART Chipman etal. Kun Kuang's Homepage @ Zhejiang University - GitHub Pages Hill, Jennifer L. Bayesian nonparametric modeling for causal inference. trees. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. ^mATE an exact match in the balancing score, for observed factual outcomes. Ben-David, Shai, Blitzer, John, Crammer, Koby, Pereira, Fernando, et al. 2019. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. (2010); Chipman and McCulloch (2016), Random Forests (RF) Breiman (2001), CF Wager and Athey (2017), GANITE Yoon etal. The central role of the propensity score in observational studies for experimental data. to install the perfect_match package and the python dependencies. 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. Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. Create a folder to hold the experimental results. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 367 0 obj In, All Holdings within the ACM Digital Library. treatments under the conditional independence assumption. =1(k2)k1i=0i1j=0^ATE,i,jt 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). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. %PDF-1.5 by learning decomposed representation of confounders and non-confounders, and Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k Learning Disentangled Representations for CounterFactual Regression Negar Hassanpour, Russell Greiner 25 Sep 2019, 12:15 (modified: 11 Mar 2020, 00:33) ICLR 2020 Conference Blind Submission Readers: Everyone Keywords: Counterfactual Regression, Causal Effect Estimation, Selection Bias, Off-policy Learning How well does PM cope with an increasing treatment assignment bias in the observed data? We repeated experiments on IHDP and News 1000 and 50 times, respectively. (2018), Balancing Neural Network (BNN) Johansson etal. stream In these situations, methods for estimating causal effects from observational data are of paramount importance. LauraE. Bothwell, JeremyA. Greene, ScottH. Podolsky, and DavidS. Jones. Higher values of indicate a higher expected assignment bias depending on yj. i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR << /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>] >> PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. In addition, we extended the TARNET architecture and the PEHE metric to settings with more than two treatments, and introduced a nearest neighbour approximation of PEHE and mPEHE that can be used for model selection without having access to counterfactual outcomes. endobj We extended the original dataset specification in Johansson etal. You can add new benchmarks by implementing the benchmark interface, see e.g. Your results should match those found in the. xTn0+H6:iUNAMlm-*P@3,K)WL The News dataset was first proposed as a benchmark for counterfactual inference by Johansson etal. 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. NPCI: Non-parametrics for causal inference. DanielE Ho, Kosuke Imai, Gary King, and ElizabethA Stuart. 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 If you reference or use our methodology, code or results in your work, please consider citing: This project was designed for use with Python 2.7. Shalit etal. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. 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). The distribution of samples may therefore differ significantly between the treated group and the overall population. 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. random forests. !lTv[ sj The ATE measures the average difference in effect across the whole population (Appendix B). Limits of estimating heterogeneous treatment effects: Guidelines for 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. ;'/ (2017). This is sometimes referred to as bandit feedback (Beygelzimer et al.,2010). The original experiments reported in our paper were run on Intel CPUs. 1 Paper We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". Mansour, Yishay, Mohri, Mehryar, and Rostamizadeh, Afshin. Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework, EMNLP, 2022. Doubly robust policy evaluation and learning. medication?". }Qm4;)v 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). Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. 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. d909b/perfect_match - Github In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Tian, Lu, Alizadeh, Ash A, Gentles, Andrew J, and Tibshirani, Robert. 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. 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. 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. All datasets with the exception of IHDP were split into a training (63%), validation (27%) and test set (10% of samples). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. You can use pip install . (2017), Counterfactual Regression Network using the Wasserstein regulariser (CFRNETWass) Shalit etal. We found that PM better conforms to the desired behavior than PSMPM and PSMMI. (2016), TARNET Shalit etal. Newman, David. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). 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. 368 0 obj We then randomly pick k+1 centroids in topic space, with k centroids zj per viewing device and one control centroid zc. You can look at the slides here. M.Blondel, P.Prettenhofer, R.Weiss, V.Dubourg, J.Vanderplas, A.Passos, 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. Navigate to the directory containing this file. By modeling the different relations among variables, treatment and outcome, we The source code for this work is available at https://github.com/d909b/perfect_match. arXiv as responsive web pages so you Candidate at the Saarland University Graduate School of Computer Science, where he is advised by Dietrich Klakow. Gani, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, Franois, Marchand, Mario, and Lempitsky, Victor. To rectify this problem, we use a nearest neighbour approximation ^NN-PEHE of the ^PEHE metric for the binary Shalit etal. (2007). A literature survey on domain adaptation of statistical classifiers. How does the relative number of matched samples within a minibatch affect performance? The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. XBART: Accelerated Bayesian additive regression trees. Simulated data has been used as the input to PrepareData.py which would be followed by the execution of Run.py. << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> cq?g Estimating individual treatment effect: Generalization bounds and Propensity Dropout (PD) Alaa etal. Federated unsupervised representation learning, FITEE, 2022. "7B}GgRvsp;"DD-NK}si5zU`"98}02 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?

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