Adversarial balancing-based representation learning for causal effect inference with observational data
TL;DR: In this paper, a neural network framework called Adversarial balancing-based representation learning for Causal Effect Inference (ABCEI) is proposed to estimate the conditional average treatment effect (CATE) from observational data.
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Abstract: Learning causal effects from observational data greatly benefits a variety of domains such as health care, education, and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist clinical planning and improve the survival rate. In this paper, we focus on studying the problem of estimating the Conditional Average Treatment Effect (CATE) from observational data. The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, in the presence of confounding bias; on the other hand, we have to deal with the identification of the CATE when the distributions of covariates over the treatment group units and the control units are imbalanced. To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on recent advances in representation learning. To ensure the identification of the CATE, ABCEI uses adversarial learning to balance the distributions of covariates in the treatment and the control group in the latent representation space, without any assumptions on the form of the treatment selection/assignment function. In addition, during the representation learning and balancing process, highly predictive information from the original covariate space might be lost. ABCEI can tackle this information loss problem by preserving useful information for predicting causal effects under the regularization of a mutual information estimator. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches. Our experiments show promising results on several datasets, encompassing several health care (and other) domains.
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Citations
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Balance Regularized Neural Network Models for Causal Effect Estimation.
Mehrdad Farajtabar,Andrew Lee,Yuanjian Feng,Vishal Gupta,Peter Dolan,Harish Chandran,Martin Szummer +6 more
TL;DR: This work is motivated by representation learning techniques to reduce differences between treated and untreated distributions that potentially arise due to confounding factors and regularize the model by encouraging it to predict control outcomes for individuals in the treatment group that are similar to control outcomes in the control group.
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Cycle-Balanced Representation Learning For Counterfactual Inference
TL;DR: In this article, the authors propose a novel framework based on Cycle-Balanced REpresentation learning for counterfactual inference (CBRE), which realizes a robust balanced representation for different groups using adversarial training, and meanwhile construct an information loop, such that preserve original data properties cyclically, which reduces information loss when transforming data into latent representation space.
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Contrastive Individual Treatment Effects Estimation
01 Nov 2022
TL;DR: In this paper , a contrastive individual treatment effects (CITE) estimation framework is proposed to predict the expected difference between the treatment and control outcome, which can provide a more precise solution to meeting personalized needs while enhancing prediction accuracy in machine learning tasks.
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Deep Learning of Potential Outcomes.
TL;DR: Deep Learning for Causal Inference as mentioned in this paper provides an overview of the emerging literature for causal inference using deep neural networks under the potential outcomes framework and provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images.
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Cycle-Balanced Representation Learning For Counterfactual Inference
01 Jan 2022
TL;DR: Zhou et al. as discussed by the authors proposed a novel framework based on Cycle-Balanced REpresentation learning for counterfactual inference (CBRE), which realized a robust and balanced representation for different groups using adversarial training and constructed an information loop that preserves original data properties cyclically, reducing information loss when transforming data into latent representation space.
References
Random Forests
Leo Breiman
- 01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
•Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
82.5K
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
74.4K
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
- 08 Dec 2014
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
•Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.