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. ,E^-"4nhi/dX]/hs9@A$}M\#6soa0YsR/X#+k!"uqAJ3un>e-I~8@f*M9:3qc'RzH ,` PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. 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. stream This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. ecology. 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 To run BART, Causal Forests and to reproduce the figures you need to have R installed. 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). Mansour, Yishay, Mohri, Mehryar, and Rostamizadeh, Afshin. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Counterfactual Inference | Papers With Code (2017). Due to their practical importance, there exists a wide variety of methods for estimating individual treatment effects from observational data. Learning Disentangled Representations for CounterFactual Regression 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. In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. (2017), and PD Alaa etal. Accessed: 2016-01-30. Edit social preview. =1(k2)k1i=0i1j=0^ATE,i,jt questions, such as "What would be the outcome if we gave this patient treatment t1?". Upon convergence, under assumption (1) and for N, a neural network ^f trained according to the PM algorithm is a consistent estimator of the true potential outcomes Y for each t. The optimal choice of balancing score for use in the PM algorithm depends on the properties of the dataset. 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. << /Linearized 1 /L 849041 /H [ 2447 819 ] /O 371 /E 54237 /N 78 /T 846567 >> These k-Nearest-Neighbour (kNN) methods Ho etal. The optimisation of CMGPs involves a matrix inversion of O(n3) complexity that limits their scalability. 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. 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 Your file of search results citations is now ready. Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k Learning representations for counterfactual inference | Proceedings of Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. Perfect Match: A Simple Method for Learning Representations For 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). i{6lerb@y2X8JS/qP9-8l)/LVU~[(/\l\"|o$";||e%R^~Yi:4K#)E)JRe|/TUTR d909b/perfect_match - Github E A1 ha!O5 gcO w.M8JP ? 373 0 obj His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. in Language Science and Technology from Saarland University and his A.B. that units with similar covariates xi have similar potential outcomes y. The News dataset contains data on the opinion of media consumers on news items. RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ [HJ)mD:K`G?/BPWw(a&ggl }[OvP ps@]TZP?x ;_[YN^0'5 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. 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. Are you sure you want to create this branch? 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). 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. 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. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Kevin Xia - GitHub Pages We consider the task of answering counterfactual questions such as, 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 . Causal Multi-task Gaussian Processes (CMGP) Alaa and vander Schaar (2017) apply a multi-task Gaussian Process to ITE estimation. Bag of words data set. 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. 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. We refer to the special case of two available treatments as the binary treatment setting. Counterfactual inference enables one to answer "What if?" simultaneously 2) estimate the treatment effect in observational studies via The samples X represent news items consisting of word counts xiN, the outcome yjR is the readers opinion of the news item, and the k available treatments represent various devices that could be used for viewing, e.g. &5mO"}S~2,z3?H BGKxr gOp1b~7Z7A^:12N$PF"=.DTcuT*5(i\C,nZZq+6TR/]FyQo'I)#TFq==UX KgvAZn&W_j3`"e|>n( Estimation and inference of heterogeneous treatment effects using random forests. observed samples X, where each sample consists of p covariates xi with i[0..p1]. For the IHDP and News datasets we respectively used 30 and 10 optimisation runs for each method using randomly selected hyperparameters from predefined ranges (Appendix I). Bayesian inference of individualized treatment effects using This work was partially funded by the Swiss National Science Foundation (SNSF) project No. CSE, Chalmers University of Technology, Gteborg, Sweden . The advantage of matching on the minibatch level, rather than the dataset level Ho etal. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. To perform counterfactual inference, we require knowledge of the underlying. method can precisely identify and balance confounders, while the estimation of 2C&( ??;9xCc@e%yeym? We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. {6&m=>9wB$ stream (2018) and multiple treatment settings for model selection. 2019. Learning disentangled representations for counterfactual regression. Natural language is the extreme case of complex-structured data: one thousand mathematical dimensions still cannot capture all of the kinds of information encoded by a word in its context. Morgan, Stephen L and Winship, Christopher. inference which brings together ideas from domain adaptation and representation !lTv[ sj 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. Learning Representations for Counterfactual Inference choice without knowing what would be the feedback for other possible choices. (2017). All other results are taken from the respective original authors' manuscripts. Upon convergence, under assumption (1) and for. in Linguistics and Computation from Princeton University. 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). 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). Article . This is sometimes referred to as bandit feedback (Beygelzimer et al.,2010). Domain-adversarial training of neural networks. 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. This setup comes up in diverse areas, for example off-policy evalu-ation in reinforcement learning (Sutton & Barto,1998), comparison with previous approaches to causal inference from observational Shalit etal. Note the installation of rpy2 will fail if you do not have a working R installation on your system (see above). rk*>&TaYh%gc,(| DiJIRR?ZzfT"Zv$]}-P+"{Z4zVSNXs$kHyS$z>q*BHA"6#d.wtt3@V^SL+xm=,mh2\'UHum8Nb5gI >VtU i-zkAz~b6;]OB9:>g#{(XYW>idhKt Ben-David, Shai, Blitzer, John, Crammer, Koby, Pereira, Fernando, et al. 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. 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. Both PEHE and ATE can be trivially extended to multiple treatments by considering the average PEHE and ATE between every possible pair of treatments. "Learning representations for counterfactual inference." International conference on machine learning. PM is easy to implement, 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. This indicates that PM is effective with any low-dimensional balancing score. Causal inference using potential outcomes: Design, modeling, 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. Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. Simulated data has been used as the input to PrepareData.py which would be followed by the execution of Run.py. Run the command line configurations from the previous step in a compute environment of your choice. We repeated experiments on IHDP and News 1000 and 50 times, respectively. 371 0 obj learning. %PDF-1.5 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. 1 Paper 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. Navigate to the directory containing this file. Hill, Jennifer L. Bayesian nonparametric modeling for causal inference. << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> (2009) between treatment groups, and Counterfactual Regression Networks (CFRNET) Shalit etal. We consider a setting in which we are given N i.i.d. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. PM, in contrast, fully leverages all training samples by matching them with other samples with similar treatment propensities. We assigned a random Gaussian outcome distribution with mean jN(0.45,0.15) and standard deviation jN(0.1,0.05) to each centroid. The script will print all the command line configurations (2400 in total) you need to run to obtain the experimental results to reproduce the News results. zz !~A|66}$EPp("i n $* Scikit-learn: Machine Learning in Python. For IHDP we used exactly the same splits as previously used by Shalit etal. Jiang, Jing. Balancing those << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> Austin, Peter C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. The experiments show that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes from observational data. Repeat for all evaluated method / degree of hidden confounding combinations. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. In. Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah. dont have to squint at a PDF. Your results should match those found in the. All rights reserved. stream We are preparing your search results for download We will inform you here when the file is ready. 0 qA0)#@K5Ih-X8oYH>2{wB2(k`:0P}U)j|B5z.O{?T ;?eKS+9S!9GQAMTl/! (2017) may be used to capture non-linear relationships. bartMachine: Machine learning with Bayesian additive regression Learning-representations-for-counterfactual-inference-MyImplementation. You can also reproduce the figures in our manuscript by running the R-scripts in. Bottou, Lon, Peters, Jonas, Quinonero-Candela, Joaquin, Charles, Denis X, Chickering, D Max, Portugaly, Elon, Ray, Dipankar, Simard, Patrice, and Snelson, Ed. ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. endobj 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). We then randomly pick k+1 centroids in topic space, with k centroids zj per viewing device and one control centroid zc. ci0pf=[3@Cm*A,rY`@n 9u_\p=p'h3C'[|kvZMJ:S=9dGC-!43BA RQqr01o:xG ?7>[pM)kC2@p%Np (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. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. 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. (2000); Louizos etal. Pearl, Judea. Representation learning: A review and new perspectives. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Tian, Lu, Alizadeh, Ash A, Gentles, Andrew J, and Tibshirani, Robert. 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. Counterfactual inference enables one to answer "What if?" questions, such as "What would be the outcome if we gave this patient treatment t1?". Note that we only evaluate PM, + on X, + MLP, PSM on Jobs. For each sample, we drew ideal potential outcomes from that Gaussian outcome distribution ~yjN(j,j)+ with N(0,0.15). Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. (2018) address ITE estimation using counterfactual and ITE generators. Matching as nonparametric preprocessing for reducing model dependence We extended the original dataset specification in Johansson etal. endobj (2017) subsequently introduced the TARNET architecture to rectify this issue. He received his M.Sc. 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. Gretton, Arthur, Borgwardt, Karsten M., Rasch, Malte J., Schlkopf, Bernhard, and Smola, Alexander. The chosen architecture plays a key role in the performance of neural networks when attempting to learn representations for counterfactual inference Shalit etal. https://cran.r-project.org/package=BayesTree/, 2016. smartphone, tablet, desktop, television or others Johansson 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. Counterfactual inference from observational data always requires further assumptions about the data-generating process Pearl (2009); Peters etal. individual treatment effects. 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. cq?g The ATE is not as important as PEHE for models optimised for ITE estimation, but can be a useful indicator of how well an ITE estimator performs at comparing two treatments across the entire population. MarkR Montgomery, Michele Gragnolati, KathleenA Burke, and Edmundo Paredes. We focus on counterfactual questions raised by what areknown asobservational studies. You can use pip install . \includegraphics[width=0.25]img/nn_pehe. Propensity Dropout (PD) Alaa etal. (3). By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output. On the binary News-2, PM outperformed all other methods in terms of PEHE and ATE. The topic for this semester at the machine learning seminar was causal inference. [Takeuchi et al., 2021] Takeuchi, Koh, et al. Prentice, Ross. Identification and estimation of causal effects of multiple 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. 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. 372 0 obj Weiss, Jeremy C, Kuusisto, Finn, Boyd, Kendrick, Lui, Jie, and Page, David C. Machine learning for treatment assignment: Improving individualized risk attribution. Doubly robust policy evaluation and learning. Learning representations for counterfactual inference - ICML, 2016. 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). This shows that propensity score matching within a batch is indeed effective at improving the training of neural networks for counterfactual inference. data that has not been collected in a randomised experiment, on the other hand, is often readily available in large quantities. 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. Schlkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. The shared layers are trained on all samples. 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. Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. 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. (2017). "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference." arXiv preprint arXiv:2102.03980, 2021. Perfect Match: A Simple Method for Learning Representations For Flexible and expressive models for learning counterfactual representations that generalise to settings with multiple available treatments could potentially facilitate the derivation of valuable insights from observational data in several important domains, such as healthcare, economics and public policy. random forests. causal effects. The role of the propensity score in estimating dose-response The script will print all the command line configurations (40 in total) you need to run to obtain the experimental results to reproduce the Jobs results. Doubly robust policy evaluation and learning. Domain adaptation: Learning bounds and algorithms. in Linguistics and Computation from Princeton University. Bang, Heejung and Robins, James M. Doubly robust estimation in missing data and causal inference models. Home Browse by Title Proceedings ICML'16 Learning representations for counterfactual inference. BayesTree: Bayesian additive regression trees. In this talk I presented and discussed a paper which aimed at developping a framework for factual and counterfactual inference. Gani, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, Franois, Marchand, Mario, and Lempitsky, Victor. (2017); Schuler etal. More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. 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. NPCI: Non-parametrics for causal inference, 2016. 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. Rubin, Donald B. Causal inference using potential outcomes. (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. While the underlying idea behind PM is simple and effective, it has, to the best of our knowledge, not yet been explored. 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.
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