时空双重机器学习用于估算柬埔寨土地出让对滥伐森林的影响
Spatiotemporal double machine learning to estimate the impact of Cambodian land concessions on deforestation
AI总结：
本文提出改进的时空双重机器学习方法，用于评估柬埔寨土地出让对森林砍伐的影响，通过处理未观察空间混杂因素提升因果推断效果。
摘要：
环境政策评估经常需要在因果推断中仔细考虑空间和时间因素。我们使用新颖的统计方法来分析柬埔寨土地出让对滥伐率的因果效应。标准方法，如差分-差异回归，在某些条件下能有效处理时空相关的处理因素，但其在考虑影响处理和结果的未观察混杂因素方面能力有限。双重空间回归（DSR）是一种利用双重机器学习处理这些情况的方法。DSR通过解决处理和结果的混杂变量，比较残差以估计处理效果。我们通过考虑时间来改进DSR，以分析具有空间效应的政策干预。我们使用贝叶斯加性回归树（BART）与空间嵌入进行大规模模拟研究，发现在某些条件下，我们的DSR模型在处理未观察空间混杂因素方面优于标准方法。然后，我们将方法应用于评估柬埔寨土地出让对滥伐森林的政策影响。
英文摘要：
Environmental policy evaluation frequently requires thoughtful consideration of space and time in causal inference. We use novel statistical methods to analyze the causal effect of land concessions on deforestation rates in Cambodia. Standard approaches, such as difference-in-differences regression, effectively address spatiotemporally-correlated treatments under some conditions, but they are limited in their ability to account for unobserved confounders affecting both treatment and outcome. Double Spatial Regression (DSR) is an approach that uses double machine learning to address these scenarios. DSR resolves the confounding variables for both treatment and outcome, comparing the residuals to estimate treatment effectiveness. We improve upon DSR by considering time in our analysis of policy interventions with spatial effects. We conduct a large-scale simulation study using Bayesian Additive Regression Trees (BART) with spatial embeddings and find that, under certain conditions, our DSR model outperforms standard approaches for addressing unobserved spatial confounding. We then apply our method to evaluate the policy impacts of land concessions on deforestation in Cambodia.
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