Causal inference methods are widely applied in various decision-making domains such as precision medicine, optimal policy and economics. Central to these applications is the treatment effect estimation of intervention strategies. Current estimation methods are mostly restricted to the deterministic treatment, which however, is unable to address the stochastic space treatment policies. Moreover, previous methods can only make binary yes-or-no decisions based on the treatment effect, lacking the capability of providing fine-grained effect estimation degree to explain the process of decision making. In our study, we therefore advance the causal inference research to estimate stochastic intervention effect by devising a new stochastic propensity score and stochastic intervention effect estimator (SIE). Meanwhile, we design a customized genetic algorithm specific to stochastic intervention effect (Ge-SIO) with the aim of providing causal evidence for decision making. We provide the theoretical analysis and conduct an empirical study to justify that our proposed measures and algorithms can achieve a significant performance lift in comparison with state-of-the-art baselines.
翻译:原因推断方法广泛应用于各种决策领域,如精密医学、最佳政策和经济学等。这些应用的核心是干预战略的治疗效果估计。目前的估算方法主要局限于确定治疗,然而,它无法解决随机空间处理政策。此外,以往的方法只能根据治疗效果作出二进制的 " 或 " 不 " 决定,缺乏提供微量效应估计度的能力来解释决策过程。因此,在我们的研究中,我们通过设计新的随机性常量分和随机干预效果估计仪(SIE),推进了估计随机干预效果的因果推断研究。与此同时,我们设计了一种针对随机干预效果的定制遗传算法(Ge-SIO),目的是为决策提供因果关系证据。我们提供了理论分析,并进行了经验研究,以证明我们提出的措施和算法能够与最新基线相比实现显著的绩效提升。