As quadcopters become essential in fields like power line inspections and aerial photography, the need for autonomous obstacle avoidance navigation grows, yet achieving human-level flexibility and safety remains a significant challenge. To solve this challenge, we propose a novel approach to autonomous obstacle avoidance for quadrotor drones in low-altitude environments using a causal imitation learning-based navigation algorithm. An improved A* algorithm is employed to generate expert trajectories that prioritize safety by optimizing the heuristic function to account for both the goal distance and proximity to obstacles. This modification reduces the aggressive path selection issue inherent in traditional A*, resulting in safer navigation and higher success rates during high-speed flight in complex environments. To further enhance generalization, a causal structure graph is constructed to address causal confounding in sequential image data. A causal structure search algorithm, based on the actor-critic method, effectively identifies hidden confounders, improving the apprentice network’s performance in both training and test environments. This approach significantly boosts the robustness and generalizability of the learned policy, making it suitable for real-world deployment.