arXiv subjects

Shalini Jayanetti

Publications and source records attributed to Shalini Jayanetti.

2 recordsLinked to original sources

arXiv2026

Marginal Structural Models for Electricity Demand under Treatment-Confounder Feedback: A Continuous-Treatment Outcome-Adaptive and Fused LASSO Approach

Temperature is the most important meteorological driver of electricity demand, but its causal effect has not been estimated under the time-varying confounding that characterizes weather processes. Current temperature is associated with weather conditions, including precipitation, snow cover, and cloud cover, that also influence electricity demand, and past temperature shapes future weather, producing treatment-confounder feedback under which standard regression adjustment is biased, due to conditioning on a time-varying confounder. We estimate the causal effect of temperature on daily Ontario electricity demand from 2018 to 2019 using a marginal structural model with inverse-probability weighting, formulated for a single observed time series rather than a panel of independent subjects, and we extend the longitudinal outcome-adaptive LASSO and adaptive fused LASSO, previously developed for binary treatments, to a continuous treatment using density-ratio weights and a weighted-covariance balance criterion. In a Monte Carlo study, unadjusted regression is biased toward the null with coverage of $0.12$ to $0.15$, whereas the stabilized outcome-adaptive estimators are nearly unbiased with coverage near $0.92$, and the cumulative three-day estimators are approximately unbiased but less efficient. In the Ontario data all estimators identify a positive and highly significant quadratic temperature effect; the unadjusted estimate is $9.34$, the stabilized and outcome-adaptive single-lag estimators give $9.8$ to $10.1$, and the cumulative estimators give smaller values that coincide with a sharp fall in effective sample size and near-unit air-density collinearity. The temperature effect on demand is therefore large and robust to single-lag confounding adjustment, while the cumulative estimates are compromised by positivity limitation that outcome-adaptive selection cannot remove.

stat.ME↗

arXiv2026

Causal Mediation Analysis for an Interrupted Time Series: Stabilized Mediator Weighting with an Application to a Vehicle Emissions Policy

Population-level policies are introduced at a fixed time and evaluated from a single series of aggregate outcomes, and the interrupted time series design estimates the total shift in an outcome after the intervention. When the policy is expected to act through a measurable pathway, the total effect is less informative than its decomposition into direct and indirect effects. We formulate causal mediation for a single interrupted time series and study stabilized mediator weighting as the estimator of the natural direct and indirect effects. Because the intervention is a deterministic function, the exposure weight equals one and the exposure contrast is identified through the segmented-regression level shift, so only the mediator pathway is weighted. We add a cumulative mediator weight that carries the lagged confounder history, incorporate a concurrent event as a second interruption, and replace variance formulas that treat the estimated weights as fixed with a block-residual bootstrap that keeps the deterministic exposure timing intact and resamples the mediator and outcome residuals in moving blocks. In a simulation calibrated to daily data, the unweighted product-of-coefficients estimator is biased under mediator-outcome confounding, with an indirect-effect bias near $0.19$ and coverage of $0.003$, whereas stabilized weighting reduces the bias to about $0.03$ and improves indirect-effect coverage from near zero to about $0.83$. Applied to the 2019 termination of Ontario's Drive Clean vehicle emissions testing program, the method estimates a direct reduction in ground-level ozone of $2.113$ parts per billion (95\% interval $-3.384$ to $-0.841$) that is robust across four Toronto regions, a positive but heterogeneous indirect effect through nitrogen dioxide, and a total effect near the boundary of significance; a pre-pandemic sensitivity analysis agrees with the primary results.

stat.ME↗