自动出价系统在预算约束下学习未知广告价值与竞标上限，实现高效决策。

We study the operational problem of automated bidding in repeated first-price auctions under budget and return-on-spend (RoS) constraints. In this setting, an auto-bidder must translate advertiser goals and constraints into real-time bids while learning two latent objects: the causal uplift value of each ad impression and the highest competing bid (HoB) needed to win it. We model uplift values and HoBs through a shared-context Linear Treatment Effect (LTE) structure and analyze both full-information and binary HoB feedback. We develop Dual-LTE, a dual-aware online learning framework that coordinates value estimation, HoB estimation, and budget/RoS control through confidence-guided exploration. We prove regret and constraint-violation guarantees that scale as $\widetilde{O}(\sqrt{T})$ under full-information HoB feedback and $\widetilde{O}(T^{2/3})$ under binary win/loss feedback, where $\widetilde{O}(\cdot)$ hides problem-dependent and logarithmic factors. Semi-synthetic experiments using real auction covariates show that Dual-LTE achieves lower regret than the baselines across budget and RoS settings, while illustrating the tradeoff between regret and constraint violation. Our results provide operational guidance for DSPs and platform auto-bidders that manage advertiser budgets or seek to meet ROAS targets. When impression values must be learned, value estimation should be coordinated with budget or ROAS control: the auto-bidder should follow the Lagrangian bidding rule only when value estimates are sufficiently accurate given the current constraint pressure and should otherwise use controlled exploration.