(2007), BART Chipman etal. 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. Learning Disentangled Representations for CounterFactual Regression %PDF-1.5 In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. 371 0 obj To elucidate to what degree this is the case when using the matching-based methods we compared, we evaluated the respective training dynamics of PM, PSMPM and PSMMI (Figure 3). 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. CSE, Chalmers University of Technology, Gteborg, Sweden. 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. Jiang, Jing. treatments under the conditional independence assumption. Domain adaptation: Learning bounds and algorithms. (2017). All datasets with the exception of IHDP were split into a training (63%), validation (27%) and test set (10% of samples). Hw(a? We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. Chernozhukov, Victor, Fernndez-Val, Ivn, and Melly, Blaise. 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). comparison with previous approaches to causal inference from observational Shalit etal. 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. Brookhart, and Marie Davidian. (2017) subsequently introduced the TARNET architecture to rectify this issue. Counterfactual Inference | Papers With Code << /Filter /FlateDecode /Length 529 >> Domain adaptation for statistical classifiers. 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). In, All Holdings within the ACM Digital Library. E A1 ha!O5 gcO w.M8JP ? However, they are predominantly focused on the most basic setting with exactly two available treatments. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. @E)\a6Hk$$x9B]aV`'iuD (2017) adjusts the regularisation for each sample during training depending on its treatment propensity. (2011). Run the command line configurations from the previous step in a compute environment of your choice. Scatterplots show a subsample of 1400 data points. Jonas Peters, Dominik Janzing, and Bernhard Schlkopf. For IHDP we used exactly the same splits as previously used by Shalit etal. Rubin, Donald B. Estimating causal effects of treatments in randomized and nonrandomized studies. (2009) between treatment groups, and Counterfactual Regression Networks (CFRNET) Shalit etal. Learning representations for counterfactual inference | Proceedings of 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. 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 the treatment effect in observational studies via counterfactual inference. 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. These k-Nearest-Neighbour (kNN) methods Ho etal. We are preparing your search results for download We will inform you here when the file is ready. cq?g Add a /Length 3974 Representation learning: A review and new perspectives. 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. general, not all the observed variables are confounders which are the common 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. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. Morgan, Stephen L and Winship, Christopher. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. Causal effect inference with deep latent-variable models. Navigate to the directory containing this file. 2023 Neural Causal Models for Counterfactual Identification and Estimation Xia, K., Pan, Y., and Bareinboim, E. (ICLR-23) In Proceedings of the 11th Eleventh International Conference on Learning Representations, Feb 2023 [ pdf , arXiv ] 2022 Causal Transportability for Visual Recognition 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. PSMMI was overfitting to the treated group. arXiv as responsive web pages so you 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 refer to the special case of two available treatments as the binary treatment setting. Max Welling. "Learning representations for counterfactual inference." International conference on machine learning. xZY~S[!-"v].8 g9^|94>nKW{[/_=_U{QJUE8>?j+du(KV7>y+ya 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). arXiv Vanity renders academic papers from stream The ACM Digital Library is published by the Association for Computing Machinery. Matching as nonparametric preprocessing for reducing model dependence 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. Once you have completed the experiments, you can calculate the summary statistics (mean +- standard deviation) over all the repeated runs using the. endobj 368 0 obj In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. the treatment effect performs better than the state-of-the-art methods on both You signed in with another tab or window. Children that did not receive specialist visits were part of a control group. 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. << /Filter /FlateDecode /Length1 1669 /Length2 8175 /Length3 0 /Length 9251 >> How do the learning dynamics of minibatch matching compare to dataset-level matching? In TARNET, the jth head network is only trained on samples from treatment tj. Jennifer L Hill. You can use pip install . In The 22nd International Conference on Artificial Intelligence and Statistics. 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. Bayesian nonparametric modeling for causal inference. ecology. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. This repository contains the source code used to evaluate PM and most of the existing state-of-the-art methods at the time of publication of our manuscript. Learning Representations for Counterfactual Inference Fredrik D.Johansson, Uri Shalit, David Sontag [1] Benjamin Dubois-Taine Feb 12th, 2020 . Analysis of representations for domain adaptation. A simple method for estimating interactions between a treatment and a large number of covariates. Note that we ran several thousand experiments which can take a while if evaluated sequentially. The propensity score with continuous treatments. (2017). 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. Date: February 12, 2020. stream The source code for this work is available at https://github.com/d909b/perfect_match. Daume III, Hal and Marcu, Daniel. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. PMLR, 1130--1138. (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). CSE, Chalmers University of Technology, Gteborg, Sweden . endobj =0 indicates no assignment bias. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. KO{J4X>+nv^m.U_B;K'pr4])||&ha~2/r5vg9(uT7uo%ztr',a3dZX.6"{3 `1QkP "n3^}. If a patient is given a treatment to treat her symptoms, we never observe what would have happened if the patient was prescribed a potential alternative treatment in the same situation. 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. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". Among States that did not Expand Medicaid, CETransformer: Casual Effect Estimation via Transformer Based In addition, using PM with the TARNET architecture outperformed the MLP (+ MLP) in almost all cases, with the exception of the low-dimensional IHDP. Matching methods are among the conceptually simplest approaches to estimating ITEs. 2019. inference. The script will print all the command line configurations (450 in total) you need to run to obtain the experimental results to reproduce the News results. 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. https://dl.acm.org/doi/abs/10.5555/3045390.3045708. Rosenbaum, Paul R and Rubin, Donald B. LauraE. Bothwell, JeremyA. Greene, ScottH. Podolsky, and DavidS. Jones.
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