All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples
模型皆有误，何者有用？有限样本中参数与非参数因果效应估计的比较AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确比较平均治疗效应的参数与非参数估计及其有限样本推断表现。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
模型皆有误，何者有用？有限样本中参数与非参数因果效应估计的比较
- 研究问题
- 在广泛而合理的数据分布中，有限样本平均治疗效应估计使用灵活非参数方法，相较简单参数方法能获得多大实际收益？
- 方法与思路
- 作者从带半信息先验的非参数模型抽取数千种生成机制，比较参数 g 计算、逆概率加权、贝叶斯树和目标最小损失估计。
- 材料陈述的贡献
- 摘要称，非参数估计在所考察机制中几乎总是表现更好，但在样本量一百时，偏差相近，区间覆盖相近或略差。
- 阅读关注点
- 应核对生成机制及先验如何界定合理分布、各估计器与区间的实现，以及偏差、均方误差和覆盖率的比较是否公平且稳定。
- 阅读边界
- 依据的是 arXiv 预印本摘要，同行评审及期刊接收未核实；所谓通用模拟仍受生成模型与先验限制，不能证明非参数方法在所有场景占优。
原文摘要
There is a long-standing debate in the statistical, epidemiological and econometric fields as to whether nonparametric estimation that uses data-adaptive methods, like machine learning algorithms in model fitting, confer any meaningful advantage over simpler, parametric approaches in real-world, finite sample estimation of causal effects. We address the question: when trying to estimate the effect of a treatment on an outcome, across a universe of reasonable data distributions, how much does the choice of nonparametric vs.~parametric estimation matter? Instead of answering this question with simulations that reflect a few chosen data scenarios, we propose a novel approach evaluating performance across thousands of data-generating mechanisms drawn from non-parametric models with semi-informative priors. We call this approach a Universal Monte-Carlo Simulation. We compare performance of estimating the average treatment effect across two parametric estimators (a g-computation estimator that uses a parametric outcome model and an inverse probability of treatment weighted estimator) and two nonparametric estimators (Bayesian additive regression trees and a targeted minimum loss-based estimator that uses an ensemble of machine learning algorithms in model fitting). We summarize estimator performance in terms of bias, confidence interval coverage, and mean squared error. We find that the nonparametric estimators nearly always outperform the parametric estimators with the exception of having similar performance in terms of bias and similar-to-slightly-worse performance in terms of coverage under the smallest sample size of N=100.