Pearl, Judea. Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. Learning disentangled representations for counterfactual regression. stream [width=0.25]img/mse 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 The outcomes were simulated using the NPCI package from Dorie (2016)222We used the same simulated outcomes as Shalit etal. Check if you have access through your login credentials or your institution to get full access on this article. data is confounder identification and balancing. (2018) address ITE estimation using counterfactual and ITE generators. 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). task. 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. Learning Representations for Counterfactual Inference | OpenReview This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. See https://www.r-project.org/ for installation instructions. The optimisation of CMGPs involves a matrix inversion of O(n3) complexity that limits their scalability. https://cran.r-project.org/package=BayesTree/, 2016. Due to their practical importance, there exists a wide variety of methods for estimating individual treatment effects from observational data. On IHDP, the PM variants reached the best performance in terms of PEHE, and the second best ATE after CFRNET. Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks d909b/perfect_match ICLR 2019 However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both. 1) and ATE (Appendix B) for the binary IHDP and News-2 datasets, and the ^mPEHE (Eq. Doubly robust estimation of causal effects. zz !~A|66}$EPp("i n $* HughA Chipman, EdwardI George, RobertE McCulloch, etal. Generative Adversarial Nets. the treatment effect performs better than the state-of-the-art methods on both Jinsung Yoon, James Jordon, and Mihaela vander Schaar. Bang, Heejung and Robins, James M. Doubly robust estimation in missing data and causal inference models. Bottou, Lon, Peters, Jonas, Quinonero-Candela, Joaquin, Charles, Denis X, Chickering, D Max, Portugaly, Elon, Ray, Dipankar, Simard, Patrice, and Snelson, Ed. (2000); Louizos etal. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. - Learning-representations-for-counterfactual-inference-. 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. Batch learning from logged bandit feedback through counterfactual risk minimization. We use cookies to ensure that we give you the best experience on our website. In The 22nd International Conference on Artificial Intelligence and Statistics. 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. Federated unsupervised representation learning, FITEE, 2022. 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. We also evaluated PM with a multi-layer perceptron (+ MLP) that received the treatment index tj as an input instead of using a TARNET. Rosenbaum, Paul R and Rubin, Donald B. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and Papers With Code is a free resource with all data licensed under. state-of-the-art. (2017). Domain adaptation: Learning bounds and algorithms. 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. Perfect Match: A Simple Method for Learning Representations For Our empirical results demonstrate that the proposed Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. Doubly robust policy evaluation and learning. (2011), is that it reduces the variance during training which in turn leads to better expected performance for counterfactual inference (Appendix E). (2011) to estimate p(t|X) for PM on the training set. Assessing the Gold Standard Lessons from the History of RCTs. The shared layers are trained on all samples. A Simple Method for Learning Representations For Counterfactual 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. (2017); Alaa and Schaar (2018). We reassigned outcomes and treatments with a new random seed for each repetition. PSMMI was overfitting to the treated group. x4k6Q0z7F56K.HtB$w}s{y_5\{_{? 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. (2011). 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. You can add new benchmarks by implementing the benchmark interface, see e.g. You can use pip install . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. 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. \includegraphics[width=0.25]img/nn_pehe. We perform extensive experiments on semi-synthetic, real-world data in settings with two and more treatments. Sign up to our mailing list for occasional updates. Estimation and inference of heterogeneous treatment effects using Share on In International Conference on Learning Representations. ^mATE endstream We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. In TARNET, the jth head network is only trained on samples from treatment tj. Bayesian nonparametric modeling for causal inference. Children that did not receive specialist visits were part of a control group. 4. Note that we only evaluate PM, + on X, + MLP, PSM on Jobs. Limits of estimating heterogeneous treatment effects: Guidelines for 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. PDF Learning Representations for Counterfactual Inference - arXiv XBART: Accelerated Bayesian additive regression trees. 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 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. that units with similar covariates xi have similar potential outcomes y. Are you sure you want to create this branch? PDF Learning Representations for Counterfactual Inference MicheleJonsson Funk, Daniel Westreich, Chris Wiesen, Til Strmer, M.Alan LauraE. Bothwell, JeremyA. Greene, ScottH. Podolsky, and DavidS. Jones. Learning representations for counterfactual inference | Proceedings of Austin, Peter C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. (2010); Chipman and McCulloch (2016) and Causal Forests (CF) Wager and Athey (2017). We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. Swaminathan, Adith and Joachims, Thorsten. The set of available treatments can contain two or more treatments. PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. We can not guarantee and have not tested compability with Python 3. =1(k2)k1i=0i1j=0^PEHE,i,j In the first part of this talk, I will present my completed and ongoing work on how computers can learn useful representations of linguistic units, especially in the case in which units at different levels, such as a word and the underlying event it describes, must work together within a speech recognizer, translator, or search engine. Counterfactual Inference | Papers With Code in parametric causal inference. The ACM Digital Library is published by the Association for Computing Machinery. NPCI: Non-parametrics for causal inference, 2016. Inference on counterfactual distributions. ecology. Authors: Fredrik D. Johansson. 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. 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. Daume III, Hal and Marcu, Daniel. Run the following scripts to obtain mse.txt, pehe.txt and nn_pehe.txt for use with the. Pi,&t#,RF;NCil6 !M)Ehc! 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. Learning representations for counterfactual inference - ICML, 2016. cq?g 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. Does model selection by NN-PEHE outperform selection by factual MSE? xTn0+H6:iUNAMlm-*P@3,K)WL 2011. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Cortes, Corinna and Mohri, Mehryar. The central role of the propensity score in observational studies for causal effects. MarkR Montgomery, Michele Gragnolati, KathleenA Burke, and Edmundo Paredes. 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. Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". For IHDP we used exactly the same splits as previously used by Shalit etal. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. 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. (2017) (Appendix H) to the multiple treatment setting. We then randomly pick k+1 centroids in topic space, with k centroids zj per viewing device and one control centroid zc. 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. Repeat for all evaluated percentages of matched samples. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. How do the learning dynamics of minibatch matching compare to dataset-level matching? (2017). endobj << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> Identification and estimation of causal effects of multiple functions. Measuring living standards with proxy variables. arXiv Vanity renders academic papers from Edit social preview. The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR PD, in essence, discounts samples that are far from equal propensity for each treatment during training. 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 . Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. /Length 3974 endobj Morgan, Stephen L and Winship, Christopher. In thispaper we propose a method to learn representations suitedfor counterfactual inference, and show its efcacy in bothsimulated and real world tasks. (2017). In these situations, methods for estimating causal effects from observational data are of paramount importance. Free Access. He received his M.Sc. Kevin Xia - GitHub Pages To manage your alert preferences, click on the button below. Shalit etal. Perfect Match (PM) is a method for learning to estimate individual treatment effect (ITE) using neural networks. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E. Contextual bandit algorithms with supervised learning guarantees. Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. 167302 within the National Research Program (NRP) 75 "Big Data". PM and the presented experiments are described in detail in our paper. We repeated experiments on IHDP and News 1000 and 50 times, respectively. However, they are predominantly focused on the most basic setting with exactly two available treatments. You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. (2007). 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. You signed in with another tab or window. NPCI: Non-parametrics for causal inference. Simulated data has been used as the input to PrepareData.py which would be followed by the execution of Run.py. Causal inference using potential outcomes: Design, modeling, In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. D.Cournapeau, M.Brucher, M.Perrot, and E.Duchesnay. trees. (2017); Schuler etal. Your results should match those found in the. In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. (2) Chernozhukov, Victor, Fernndez-Val, Ivn, and Melly, Blaise. Representation-balancing methods seek to learn a high-level representation for which the covariate distributions are balanced across treatment groups. 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. In, Strehl, Alex, Langford, John, Li, Lihong, and Kakade, Sham M. Learning from logged implicit exploration data.