A software system for causal reasoning in causal Bayesian networks

Knowing the cause and effect is important to researchers who are interested in modeling the effects of actions. One commonly used method for modeling cause and effect is graphical model. Bayesian Network is a probabilistic graphical model for representing and reasoning uncertain knowledge. A common graphical causal model used by many researchers is a directed acyclic graph (DAG) with causal interpretation known as the causal Bayesian network (BN). Causal reasoning is the causal interpretation part of a causal Bayesian Network. They enable people to find meaningful order in events that might otherwise appear random and chaotic. Further more, they can even help people to plan and predict the future. We develop a software system, which is a set of tools to solve causal reasoning problems, such as to identify unconditional causal effects, to identify conditional causal effects and to find constraints in a causal Bayesian Networks with hidden variables

Identifiability and transportability in dynamic causal networks

In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Networks, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder variables that affect in substantially different ways the identification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure for the transportability of causal effects in Dynamic Causal Network settings, where the result of causal experiments in a source domain may be used for the identification of causal effects in a target domain.Preprin

Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach

This paper studies bandit problems where an agent has access to offline data that might be utilized to potentially improve the estimation of each arm's reward distribution. A major obstacle in this setting is the existence of compound biases from the observational data. Ignoring these biases and blindly fitting a model with the biased data could even negatively affect the online learning phase. In this work, we formulate this problem from a causal perspective. First, we categorize the biases into confounding bias and selection bias based on the causal structure they imply. Next, we extract the causal bound for each arm that is robust towards compound biases from biased observational data. The derived bounds contain the ground truth mean reward and can effectively guide the bandit agent to learn a nearly-optimal decision policy. We also conduct regret analysis in both contextual and non-contextual bandit settings and show that prior causal bounds could help consistently reduce the asymptotic regret

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias

We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions. We also generalize adjustment criteria and formulas from the acyclic setting to the general one (i.e. ioSCMs). Such criteria then allow to estimate (conditional) causal effects from observational data that was (partially) gathered under selection bias and cycles. This generalizes the backdoor criterion, the selection-backdoor criterion and extensions of these to arbitrary ioSCMs. Together, our results thus enable causal reasoning in the presence of cycles, latent confounders and selection bias. Finally, we extend the ID algorithm for the identification of causal effects to ioSCMs.Comment: Accepted for publication in Conference on Uncertainty in Artificial Intelligence 2019 (UAI-2019

Achieving Causal Fairness in Recommendation

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort

Achieving Causal Fairness in Recommendation

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort