Recent advancements in causal inference are increasingly focused on enhancing the practical applicability of causal reasoning across various domains. A notable trend is the development of benchmarks and frameworks that assess the capabilities of large language models in intervention reasoning and causal study design, addressing gaps in existing methodologies. For instance, new approaches in stress testing leverage causal panel prediction to improve the robustness of financial forecasts under uncertain macroeconomic conditions. Additionally, innovative frameworks like temporal transportation and causal bottleneck models are being introduced to better estimate treatment effects over time and reduce dimensionality in causal relationships. The emergence of active causal experimentalist strategies highlights a shift towards adaptive learning in experimental design, allowing for more informed decision-making based on sequential interventions. Collectively, these efforts aim to refine causal inference techniques, making them more efficient and applicable to real-world challenges in policy-making, finance, and beyond.
Causal inference in social science relies on end-to-end, intervention-centered research-design reasoning grounded in real-world policy interventions, but current benchmarks fail to evaluate this capab...
Understanding causal dependencies in observational data is critical for informing decision-making. These relationships are often modeled as Bayesian Networks (BNs) and Directed Acyclic Graphs (DAGs). ...
Regulatory stress testing requires projecting credit losses under hypothetical macroeconomic scenarios -- a fundamentally causal question typically treated as a prediction problem. We propose a framew...
Understanding directed temporal interactions in multivariate time series is essential for interpreting complex dynamical systems and the predictive models trained on them. We present Causal-INSIGHT, a...
Understanding why real-world events occur is important for both natural language processing and practical decision-making, yet direct-cause inference remains underexplored in evidence-rich settings. T...
Treatment effects estimated from randomized controlled trials are local not only to the study population but also to the time at which the trial was conducted. We develop a framework for temporal tran...
Discovering causal relationships requires controlled experiments, but experimentalists face a sequential decision problem: each intervention reveals information that should inform what to try next. Tr...
Estimation of heterogeneous long-term treatment effects (HLTEs) is widely used for personalized decision-making in marketing, economics, and medicine, where short-term randomized experiments are often...
We introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models. At the core of SCBMs lies the assumption that causal effects between high-dimensional variables onl...
Estimating causal effects from observational data remains a fundamental challenge in causal inference, especially in the presence of latent confounders. This paper focuses on estimating causal effects...