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摘要翻译：
我们开发了一个横断面研究设计，以确定在没有仪器的情况下无法观察到的异质性存在时的因果效应。当单元在物理空间中密集时，回归治疗结果的“空间优先差异”(SFD)并省略所有协变量可能就足够了。SFD的识别假设在数学结构和合理性上与其他准实验设计相似。我们使用SFD来获得时间不变的地理因素、土壤和气候对长期农业产量的影响的新估计--这些关系对土地管理和气候政策等经济决策至关重要，但众所周知，这些关系被不可观察的因素所混淆。
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英文标题：
《Accounting for Unobservable Heterogeneity in Cross Section Using Spatial
First Differences》
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作者：
Hannah Druckenmiller and Solomon Hsiang
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments. When units are dense in physical space, it may be sufficient to regress the "spatial first differences" (SFD) of the outcome on the treatment and omit all covariates. The identifying assumptions of SFD are similar in mathematical structure and plausibility to other quasi-experimental designs. We use SFD to obtain new estimates for the effects of time-invariant geographic factors, soil and climate, on long-run agricultural productivities --- relationships crucial for economic decisions, such as land management and climate policy, but notoriously confounded by unobservables.
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PDF链接：
https://arxiv.org/pdf/1810.07216
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