• G.Prasanna Kumar

This study asks whether causal forecasting can move public finances from signal to steering. A stratified panel of 200 observations combines archival outturns with a governance survey. Two-way fixed effects explain R² = 0.509 (~51%) of variation in policy-control effectiveness. Standardized effects suggest modest, directionally coherent gains: a 1σ rise in institutional capacity associates with +0.14σ, and data quality with +0.11σ, in control effectiveness; event-time estimates show small, delayed post-adoption improvements. Lagged transparency shows a near-threshold coefficient (+0.379, p = 0.055), corresponding to implementation lags. The limitations of the approach are given by finite horizons for inference and imprecise estimates under robust inference. The implications of the analysis focus attention on evaluation of alternatives aware of uncertainty, factorial orderings of institutions. Practical benefits include playbooks ready for use for real-time governance of data, evaluating possibilities of design-based avenues of identification checks, transparent pipelines which raise incrementally the probability of meeting rules of fiscal commitment.

Causal forecasting, Institutional capacity, Policy control, Data governance, Model transparency, Public finance

"From Signal to Steering: Causal Forecasting for Effective Financial Policy Control Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.a252-a266, December-2025, Available :http://www.jetir.org/papers/JETIR2512033.pdf

"From Signal to Steering: Causal Forecasting for Effective Financial Policy Control Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppa252-a266, December-2025, Available at : http://www.jetir.org/papers/JETIR2512033.pdf