One-stage deep instrumental variable method for causal inference from observational data

© 2019 IEEE. Causal inference from observational data aims to estimate causal effects when controlled experimentation is not feasible, but it faces challenges when unobserved confounders exist. The instrumental variable method resolves this problem by introducing a variable that is correlated with the treatment and affects the outcome only through the treatment. However, existing instrumental variable methods require two stages to separately estimate the conditional treatment distribution and the outcome generating function, which is not sufficiently effective. This paper presents a one-stage approach to jointly estimate the treatment distribution and the outcome generating function through a cleverly designed deep neural network structure. This study is the first to merge the two stages to leverage the outcome to the treatment distribution estimation. Further, the new deep neural network architecture is designed with two strategies (i.e., shared and separate) of learning a confounder representation account for different observational data. Such network architecture can unveil complex relationships between confounders, treatments, and outcomes. Experimental results show that our proposed method outperforms the state-of-the-art methods. It has a wide range of applications, from medical treatment design to policy making, population regulation and beyond

Causal Inference Using Bayesian Deep Learning

University of Technology Sydney. Faculty of Engineering and Information Technology.Causal inference from observational data has wide application in precision medicine, economics, social sciences, computational advertising, and so on. Causal inference from observational data aims to estimate causal effects when controlled experimentation is not feasible. Causal inference is the process of identifying how a change in a cause leads to a change in the outcome. In today’s data–driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. Confounding bias occurring in observational data may result in causal inference leading a wrong result. Confounding bias is the fundamental bias of causal inference from observational data. Under some specific assumptions, it is possible to estimate the causal effect from observational data with confounding bias. Although the existing literature contains some excellent models, there is room to improve their representation power and their ability to capture complex causal relationships. Furthermore, there is a research gap between deep Bayesian models and causal inference from observational data under confounding bias. In order to narrow the gap, this thesis provides algorithms to estimate the causal effects from observational data in some cases when a set of confounders exists. This result can provide effective decision support for policymakers in various areas. This thesis recovers causal inference from observational data with observed confounding bias, unobserved confounding bias and time-dependent confounding bias. First, this thesis considers two kinds of causal inference problems when observed confounding bias exists. This thesis proposes a model with separate Gaussian processes to estimate the Conditional Average Causal Effect on the Treated (CACT). Each separate Gaussian process is proposed to estimate the average causal effect for the treated group and the control group. In order to estimate various kinds of causal effects, such as average, conditional average, and average treated, this thesis focuses on Bayesian generative models. A prior called Causal DP is proposed, and a generative model called CDP based on the prior is developed to estimate causal effects. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data. The model is a Bayesian nonparametric generative model and is not based on the assumption of any parametric distribution. The proposed generative model performs well with missing covariates and does not suffer from overfitting. Second, this thesis proposes methods to resolve the challenges when unobserved confounding bias exists. The instrumental variable methods resolve this problem by introducing a variable that is correlated with the treatment and affects the outcome only through the treatment. This thesis presents a one-stage approach to jointly estimate the treatment distribution and the outcome generating function through a designed deep neural network structure. The one-stage method is different to existing instrumental variable methods requiring two stages to separately estimate the conditional treatment distribution and the outcome generating function. This study is the first to merge the two stages to leverage the outcome to the treatment distribution estimation. Finally, this thesis estimates the causal effect for Dynamic Treatment Regimes (DTRs) where time-dependent confounding bias exists. Censoring and time-dependent confounding under DTRs bring a challenge in the observational data has a declining sample size but an increasing feature dimension over time. This thesis combines outcome regression models with treatment models for high-dimensional features using uncensored subjects that are potentially small-sample. And this thesis fits deep Bayesian models for outcome regression models to unveil complex relationships between confounders, treatments, and outcomes. This thesis evaluates all the methods proposed in this thesis using synthetic, semisynthetic or real-world data. Comparative experiments against several state-of-the-art methods show that the proposed methods generally perform better than or are comparative with their competitors. Given the key importance of causal inference in both theory and real-world applications, we argue that the models and algorithms proposed in this thesis contribute to both scientific research and practical applications

False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments

Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose statistical methods that can systematically and accurately identify Heterogeneous Treatment Effect (HTE) of any user cohort of interest (e.g. mobile device type, country), and determine which factors (e.g. age, gender) of users contribute to the heterogeneity of the treatment effect in an A/B test. By applying these methods on both simulation data and real-world experimentation data, we show how they work robustly with controlled low False Discover Rate (FDR), and at the same time, provides us with useful insights about the heterogeneity of identified user groups. We have deployed a toolkit based on these methods, and have used it to measure the Heterogeneous Treatment Effect of many A/B tests at Snap

Deep Causal Learning: Representation, Discovery and Inference

Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial optimization problems, unknown intervention, unobserved confounders, selection bias and estimation bias. Deep causal learning, that is, causal learning based on deep neural networks, brings new insights for addressing these problems. While many deep learning-based causal discovery and causal inference methods have been proposed, there is a lack of reviews exploring the internal mechanism of deep learning to improve causal learning. In this article, we comprehensively review how deep learning can contribute to causal learning by addressing conventional challenges from three aspects: representation, discovery, and inference. We point out that deep causal learning is important for the theoretical extension and application expansion of causal science and is also an indispensable part of general artificial intelligence. We conclude the article with a summary of open issues and potential directions for future work