提出新方法精准分离治疗对不同人群的差异影响
Consistent Causal Inference of Group Effects in Non-Targeted Trials with Finitely Many Effect Levels
- 先聚类后合并，非参数化方法分离治疗在不同群体的效果
- 合成数据上准确率提升超10倍，优于现有最佳方法
- 适合需区分治疗正负效应的临床试验研究者
某治疗可能对特定群体（病患）产生正面效果，却对另一群体（健康者）造成负面影响。在非靶向试验中，病患与健康者均被纳入治疗组，导致治疗效果呈现异质性。此时，准确推断治疗对病患群体的真实效果变得困难，因各群体效应相互混淆。本文提出一种高效非参数方法——PCM（预聚类并合并），用于估计群体效应。我们证明了该方法在一般设定下的渐近一致性，并在合成数据上验证其准确率超过现有最优方法10倍以上。该方法可更广泛应用于有限取值函数的一致估计。
原文摘要 · Abstract (English)
A treatment may be appropriate for some group (the ``sick" group) on whom it has a positive effect, but it can also have a detrimental effect on subjects from another group (the ``healthy" group). In a non-targeted trial both sick and healthy subjects may be treated, producing heterogeneous effects within the treated group. Inferring the correct treatment effect on the sick population is then difficult, because the effects on the different groups get tangled. We propose an efficient nonparametric approach to estimating the group effects, called {\bf PCM} (pre-cluster and merge). We prove its asymptotic consistency in a general setting and show, on synthetic data, more than a 10x improvement in accuracy over existing state-of-the-art. Our approach applies more generally to consistent estimation of functions with a finite range.
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