7 papers
Market Impact Estimation via Causal Foundation Models
Dennis Thumm, Ying Chen
When a trader executes a large order, the relevant question is not \emph{what will the price be?} but \emph{what would the price have been had I not traded?} Market impact is thus…
DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series
Dennis Thumm, Billy Tim Anthony, Ying Chen
Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where…
Towards Continuous-time Causal Foundation Models
Dennis Thumm, Ruben Wiedemann, Ying Chen
Extending discrete-time causal Prior-data Fitted Networks for time series to continuous time invites writing the mechanism as a stochastic differential equation (SDE) -- but if the…
Interventional Time Series Priors for Causal Foundation Models
Dennis Thumm, Ying Chen
Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remains limited by the absence of synt…
Sequential Causal Normal Form Games: Theory, Computation, and Strategic Signaling
Dennis Thumm
Can classical game-theoretic frameworks be extended to capture the bounded rationality and causal reasoning of AI agents? We investigate this question by extending Causal Normal Fo…
Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
Market generators using deep generative models have shown promise for synthetic financial data generation, but existing approaches lack causal reasoning capabilities essential for…
Causal Regime Detection in Energy Markets With Augmented Time Series Structural Causal Models
Dennis Thumm
Energy markets exhibit complex causal relationships between weather patterns, generation technologies, and price formation, with regime changes occurring continuously rather than a…