Doubly weighted M-estimation for nonrandom assignment and missing outcomes

基于观测数据的最优动态治疗方案策略学习

Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data

针对观测数据下的最优动态治疗方案学习问题，提出一种基于分类的双重鲁棒反向归纳方法，通过最大化各阶段增广逆概率加权估计量学习策略，实现了福利遗憾的$n^{-1/2}$最优收敛速率。

在依从性不完美的协变量自适应随机化中通过回归调整提升估计效率

Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance

针对依从性不完美的协变量自适应随机化场景，提出基于双重稳健矩的回归调整估计方法，包含线性、非线性等调整形式，可提升估计效率并给出达到半参数效率界的条件。

Group Average Treatment Effects for Observational Studies

The paper proposes an estimator to make inference of heterogeneous treatment effects sorted by impact groups (GATES) for non-randomised experiments. The groups can be understood as a broader aggregation of the conditional average treatment effect (CATE) where the number of groups is set in advance. In economics, this approach is similar to pre-analysis plans. Observational studies are standard in policy evaluation from labour markets, educational surveys and other empirical studies. To control for a potential selection-bias, we implement a doubly-robust estimator in the first stage. We use mac…

基于上下文赌博机数据的自适应加权离线策略评估

Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits

本文针对上下文赌博机历史数据中目标策略与数据收集策略不一致导致DR估计量方差过大的问题，提出自适应加权改进DR估计量，并证明其t统计量渐近正态，可用于置信区间和假设检验，实验验证了其准确性与推断性能优于现有方法。

基于分布学习的加权治疗效应估计

Weighting-Based Treatment Effect Estimation via Distribution Learning

本文提出一种基于分布学习的加权方法，通过学习协变量条件分布并利用密度比作为权重，以缓解模型误设，实验表明其在估计ATT上优于现有加权方法且具有双稳健性。

动态处理效应：模型误设下的高维推断

Dynamic treatment effects: high-dimensional inference under model misspecification

针对动态处理效应估计中模型误设与高维协变量挑战，提出序贯模型双重稳健估计量，通过新颖矩目标估计与损失函数实现根号N推断。

Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression

We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models. As the development of machine learning for causal inference, a wide range of large-scale models for causality are gaining attention. One problem is that suspicions have been raised that the large-scale models are prone to overfitting to observations with sample selection, hence the large models may not be suitable for causal prediction. In this study, to resolve the suspicious, we investigate on the validity of causal inference methods for overparameterize…

配对实验中的协变量调整

Covariate Adjustment in Experiments with Matched Pairs

本文研究配对实验中平均处理效应的协变量调整，提出基于双重稳健矩条件的估计量，发现有限维线性调整未必提升精度，而配对固定效应和基于LASSO的高维调整可确保精度提升，并验证了其实际有效性。

Double machine learning for sample selection models

This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way f…

聚类网络干扰下的个体化策略评估与学习

Individualized Policy Evaluation and Learning under Clustered Network Interference

针对忽略网络干扰导致策略评估有偏的问题，提出一种基于半参数加性结构模型的方法，用于聚类网络干扰下的个体化处理规则评估与学习，并开发了高效且双重稳健的估计器以提升学习策略性能。