As vehicles advance toward full autonomy, SAE Level 5 systems are typically designed and envisioned without driver controls. While this promises convenience and safety, it also raises challenges around user trust and the discomfort of having no agency. This study investigates how explanation modality (vocal vs. text) and optional control mechanisms influence trust and intervention behavior in a simulated SAE Level 5 context. Thirty-six participants completed three VR driving scenarios that varied in explanation and control design. Trust and intervention behavior were measured alongside thematic analysis of open-ended feedback. Results showed that vocal explanations increased trust more than text, though not significantly. However, the presence of control buttons significantly enhanced trust among participants who perceived them positively. These participants also intervened less often, though the effect was not statistically significant. Exploratory analyses revealed that self-reported comfort with automation was associated with higher trust and lower intervention rates. These findings challenge the SAE Level 5 assumption of no user input. Even minimal, optional control features can foster trust and reduce unnecessary interventions. The study underscores the value of designing autonomous systems that maintain transparency and user agency, supporting safer and more acceptable human-AI interaction.

Strategy games could be considered as an amazing playground for using Causal inference methods. The complex nature of the data and the built-in randomization help with testing causal inference in a scenario where in reality it would be hard and expensive. Randomized data in coherence with causal inference is well documented and tested, but not regarding the strategy game of interest DotA 2. To evaluate the quality of causal inference using randomized data for predictions in the game, the average causal effect estimand is used. The calculation of the average causal effect of certain events between different intervals and their comparison in addition to the calculation of the statistical Independence between variables of concern comprise the bulk of the research. The calculations allow for logical deductions and statistical correlations between values to reach a conclusion. The final verdict being that causal inference with randomized data is helpful for predicting events in DotA 2 but the amount of data and existing complex biases can be deceiving and can heavily influence the results.