Counterfactual evaluation of two Austrian agri‐environmental schemes in 2014–2018
研究了奥地利两项农业环境方案对农场化肥和植保支出的因果效应，发现样本量小导致无法证实中等效果，且双重机器学习在协变量选择上无额外优势。
Abstract This article investigates the causal effect of farm participation in two Austrian agri‐environmental schemes (AES), Immergrün ( ground cover ) and Zwischenfrucht ( catch cropping ), on fertilizer and plant protection expenditures in the 2014 programming period. Combining European Farm Accountancy Data Network data with information on scheme participation from administrative control data offers identifying farm participation in specific schemes targeted at reducing input intensity. Given the overall small sample, we maximized the utilizable sample size by combining difference‐in‐difference and kernel matching with automated bandwidth selection. To address the remaining post‐matching covariate imbalances, we used double machine learning (DML) techniques for a guided selection of potential confounding covariates. Our results suggest that, given the available sample, we cannot substantiate moderate effects of AES participation, and that guided covariate selection by DML offers no gain over non‐guided covariate selection for the small sample. Our results underline the need to increase the number of farms and the duration in available farm panels to substantiate future counterfactual‐based evaluations of policy.