This project involves the impact analysis of the FIONA program on farmer profits in India. The analysis includes the assessment of assumptions, different statistical approaches, and recommendations based on the findings.
The common support assumption is validated across covariates, including crop, district, and isyoung. The analysis demonstrates that the assumption holds for these variables.
The conditional independence assumption, crucial for causal inference, cannot be directly validated due to the nature of potential outcomes. However, the analysis proceeds with SOO approaches, acknowledging this limitation.
The regression model includes relevant covariates such as crop variety, district, and farmer age. The results indicate that the ATE of FIONA on farmer profits is statistically significant.
The exact matching approach is employed with covariates iswheat, isrice, and islentils. The matched data are then used to estimate both ATE and Average Treatment on the Treated (ATT). The results are consistent with the regression-based approach.