Wintertime PM_2.5 pollution in Beijing is increasingly governed by secondary aerosol formation, with sulfate and nitrate constituting the dominant inorganic components. However, quantifying the contribution of ambient SO₂ and NO₂ to PM_2.5 formation from observational data remains challenging because precursor&ndash;PM_2.5 relationships are confounded by meteorological variability, regional transport, and unmeasured emissions. Using routine monitoring data from Beijing heating seasons during 2016&ndash;2023, we develop an episode-based causal framework that restricts the analysis to calm periods to address transport-related confounding, and estimates nonlinear precursor responses using shape-constrained additive models incorporating emission proxies and precursor effect modification.</p> <p>The proxy-calibrated model shows that combustion-related primary emissions remain the dominant unresolved confounder during calm episodes. Precursor concentrations lagged by 2&ndash;3 h explain short-term PM_2.5 changes, with faster apparent secondary processing in the pollution-hotspot southeastern urban area. Effect-modification analysis indicates that the SO₂ response is primarily regulated by thermal conditions. Annual attribution shows that SO₂ reductions dominated precursor-associated PM_2.5 improvements in the early years, but their contribution weakened as ambient SO₂ declined and its marginal effect diminished at low concentrations. In contrast, NO₂ sensitivity increases consistently at higher O₃ levels, suggesting that elevated O₃ in recent years may have enhanced nitrate-related PM_2.5 formation, consistent with the increased benefits associated with NO₂ reductions after 2020. These results indicate a transition toward increasingly nitrate-sensitive wintertime PM_2.5 formation in Beijing and show that routine monitoring data can provide chemically interpretable precursor sensitivities when transport and emission confounding are explicitly constrained.