The source code for this work is available at https://github.com/d909b/perfect_match. We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. synthetic and real-world datasets. stream PMLR, 1130--1138. inference which brings together ideas from domain adaptation and representation 1) and ATE (Appendix B) for the binary IHDP and News-2 datasets, and the ^mPEHE (Eq. (2017). PD, in essence, discounts samples that are far from equal propensity for each treatment during training. Run the following scripts to obtain mse.txt, pehe.txt and nn_pehe.txt for use with the. Max Welling. On the binary News-2, PM outperformed all other methods in terms of PEHE and ATE. In TARNET, the jth head network is only trained on samples from treatment tj. Susan Athey, Julie Tibshirani, and Stefan Wager. Finally, we show that learning rep-resentations that encourage similarity (also called balance)between the treatment and control populations leads to bet-ter counterfactual inference; this is in contrast to manymethods which attempt to create balance by re-weightingsamples (e.g., Bang & Robins, 2005; Dudk et al., 2011;Austin, 2011; Swaminathan More complex regression models, such as Treatment-Agnostic Representation Networks (TARNET) Shalit etal. 4. Are you sure you want to create this branch? We propose a new algorithmic framework for counterfactual We found that PM better conforms to the desired behavior than PSMPM and PSMMI. As a secondary metric, we consider the error ATE in estimating the average treatment effect (ATE) Hill (2011). 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. [Takeuchi et al., 2021] Takeuchi, Koh, et al. Create a folder to hold the experimental results. We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. 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. ^mATE Uri Shalit, FredrikD Johansson, and David Sontag. questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". 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. 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. !lTv[ sj Note: Create a results directory before executing Run.py. A comparison of methods for model selection when estimating 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. PDF Learning Representations for Counterfactual Inference - arXiv (2017).. Identification and estimation of causal effects of multiple
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