In the United States (U.S.), aggressive suppression policies in the 20^th century reduced wildfires in the short term, but accumulated fuels contributed to increased wildfire risk in the long term. Land managers are slowly reintroducing the use of fuel treatments, including prescribed (Rx) fires, to remove fuels and mitigate future wildfires. To date, efforts to systematically quantify the efficacy of fuel treatments for wildfire mitigation have been limited in spatial and temporal scope or rely on proxies, such as low-intensity wildfires. Here we use the 30-m Harmonized Landsat and Sentinel-2 (HLS) dataset to analyze timeseries of vegetation “greenness” indices, such as the Normalized Burn Ratio (NBR), with observations up to every 1.4 days. We apply a causal inference approach, difference-in-differences (DID), on HLS-derived timeseries of NBR to compare outcomes in treated and surrounding control areas in three different time phases: 1) post-treatment fuel reduction, 2) wildfire-induced burn severity, and 3) post-wildfire vegetation recovery. As a case study, we targeted 37 Rx fires that preceded and intersected the 2024 Park Fire, a large wildfire in northern California. We show statistical evidence that Rx fires reduce fuel loads (12 Rx fires), wildfire burn severity (12 Rx fires), and postwildfire vegetation recovery (14 Rx fires). Our approach requires only the spatial footprint and timing of the fuel treatments, thus enabling regional to nationwide analyses using a large number of fuel treatments to quantify the general efficacy of fuel treatments across a variety of treatment types, fuel types, and topography.