Economics

Causal discovery is central to enable causal models for tasks such as effect estimation, counterfactual reasoning, and root cause attribution. Yet existing approaches face trade-offs: purely statistical methods (e.g., PC, LiNGAM) often return structures that overlook domain knowledge, while expert-designed DAGs are difficult to scale and time-consuming to construct. We propose CausalFusion, a hybrid framework

Switchback experiments assign units to treatment and control over time, yielding more precise causal estimates than fixed designs but risking bias from carryover effects, where past treatments influence future outcomes. Existing estimators require specifying an influence period, i.e. an upper bound on carryover duration, often guessed from intuition. We propose a statistical test that detects when this

In A/B testing, statistical power depends on both the variance of estimated impacts and the distribution of true impacts. A low variance metric can have low power if true impacts on the metric tend to be small, while a high variance metric can have high power if true impacts on the metric tend to be large. Traditional power calculations, however, focus solely on the variance of estimated impacts. They compute

User-randomized A/B testing, while the gold standard for online experimentation, faces significant limitations when legal, ethical, or practical considerations prevent its use. Item-level randomization offers an alternative but typically suffers from high variance and low statistical power due to skewed distributions and limited sample sizes. We here introduce Regular Balanced Switchback Designs (RBSDs)

This paper examines the effectiveness of stratification in experimental design using evidence from multiple large-scale experiments. We analyze data from experiments ranging from approximately 30,000 to 180,000 units across different business contexts. Our results show that pre-stratification and post-stratification achieve virtually identical precision improvements - largest in smaller samples (10% improvement