The widely used 'Counterfactual' definition of Causal Effects was derived for unbiasedness and accuracy - and not generalizability. We propose a simple definition for the External Validity (EV) of Interventions and Counterfactuals. The definition leads to EV statistics for individual counterfactuals, and to non-parametric effect estimators for sets of counterfactuals (i.e., for samples). We use this new definition to discuss several issues that have baffled the original counterfactual formulation: out-of-sample validity, reliance on independence assumptions or estimation, concurrent estimation of multiple effects and full-models, bias-variance tradeoffs, statistical power, omitted variables, and connections to current predictive and explaining techniques. Methodologically, the definition also allows us to replace the parametric, and generally ill-posed, estimation problems that followed the counterfactual definition by combinatorial enumeration problems in non-experimental samples. We use this framework to generalize popular supervised, explaining, and causal-effect estimators, improving their performance across three dimensions (External Validity, Unconfoundness and Accuracy) and enabling their use in non-i.i.d. samples. We demonstrate gains over the state-of-the-art in out-of-sample prediction, intervention effect prediction and causal effect estimation tasks. The COVID19 pandemic highlighted the need for learning solutions to provide general predictions in small samples - many times with missing variables. We also demonstrate applications in this pressing problem.
翻译:广泛使用的“ 事实” 因果关系定义是用来说明公正性和准确性,而不是一般性的。 我们提出了干预和反事实的外部有效性的简单定义。 定义导致个人反事实的 EV 统计数据, 以及一系列反事实( 即样本)的非参数效应估计值。 我们使用这一新定义来讨论一些使最初反事实提法产生混淆的问题: 超常有效性、 依赖独立假设或估计、 多重效果和全模型的并行估计、 偏差交易、 统计实力、 省略变量以及与当前预测和解释技术的连接。 从方法上看, 定义还使我们能够用非解释性抽样抽样的分类问题来取代反事实定义之后的参数和一般估计问题。 我们使用这个框架来概括大众监督、解释和因果关系估测, 改善其在三个层面的性应用、 偏差性权衡、 统计性、 统计性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 预测性、 展示性、 预测性、 和性