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.

翻译：环境政策评估通常需要在因果推断中审慎考虑空间与时间维度。本研究采用新型统计方法分析柬埔寨土地特许权对森林砍伐率的因果效应。标准方法（如双重差分回归）在某些条件下能有效处理时空相关的处理变量，但其在控制同时影响处理变量与结果变量的未观测混杂因素方面存在局限。双重空间回归（DSR）是一种运用双重机器学习处理此类场景的方法。DSR通过解析处理变量与结果变量的混杂因素，比较残差来估计处理效应。本研究通过将时间维度纳入具有空间效应的政策干预分析，对DSR方法进行了改进。我们利用具有空间嵌入的贝叶斯加性回归树（BART）开展大规模模拟研究，发现在特定条件下，我们的DSR模型在应对未观测空间混杂问题方面优于标准方法。最后，我们应用该方法评估了柬埔寨土地特许权政策对森林砍伐的实际影响。