Causal Inference With Selectively Deconfounded Data

The Deconfounded Recommender: A Causal Inference Approach to Recommendation

The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question---for each user and movie, what would the rating be if we "forced" the user to watch the movie? To this end, we develop a causal approach to recommendation, one where watching a movie is a "treatment" and a user's rating is an "outcome." The problem is there may be unobserved confounders, variables that affect both which movies the users watch and how they rate them; unobserved confounders impede causal predictions wi…

Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding

Unobserved confounding is one of the greatest challenges for causal discovery. The case in which unobserved variables have a widespread effect on many of the observed ones is particularly difficult because most pairs of variables are conditionally dependent given any other subset, rendering the causal effect unidentifiable. In this paper we show that beyond conditional independencies, under the principle of independent mechanisms, unobserved confounding in this setting leaves a statistical footprint in the observed data distribution that allows for disentangling spurious and causal effects. Us…

预测驱动的因果推断

Prediction-Powered Causal Inferences

本研究提出预测驱动的因果推断方法，通过条件校准和表示约束实现跨实验的有效处理效应估计，并在无人工标注的科学实验中首次取得成功。

存在混淆因素的非线性因果发现

Nonlinear Causal Discovery with Confounders

本文提出DeFuSE方法，通过去混淆调整和顺序过程学习存在混淆因素的有向无环图中的非线性因果关系，并证明了其一致性，在模拟和基因调控网络分析中表现优异。

去混杂暖启动汤普森采样及其在精准医学中的应用

Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine

提出去混杂暖启动汤普森采样（DWTS），利用双重去偏LASSO利用混杂观测数据初始化LinTS先验，在合成和真实心血管数据上降低累积遗憾，提升临床试验效率并支持个性化治疗。

Causal Induction from Visual Observations for Goal Directed Tasks

Causal reasoning has been an indispensable capability for humans and other intelligent animals to interact with the physical world. In this work, we propose to endow an artificial agent with the capability of causal reasoning for completing goal-directed tasks. We develop learning-based approaches to inducing causal knowledge in the form of directed acyclic graphs, which can be used to contextualize a learned goal-conditional policy to perform tasks in novel environments with latent causal structures. We leverage attention mechanisms in our causal induction model and goal-conditional policy, e…