7 papers
From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
Riccardo Cadei, Frank Otchere, Nyasha Tirivayi +3
Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly. Existing approaches trade expressivit…
Exploratory Causal Inference in SAEnce
Tommaso Mencattini, Riccardo Cadei, Francesco Locatello
Randomized Controlled Trials are one of the pillars of science; nevertheless, they rely on hand-crafted hypotheses and expensive analysis. Such constraints prevent causal effect es…
The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations
Dingling Yao, Shimeng Huang, Riccardo Cadei +2
Causal reasoning and discovery, two fundamental tasks of causal analysis, often face challenges in applications due to the complexity, noisiness, and high-dimensionality of real-wo…
Prediction-Powered Causal Inferences
Riccardo Cadei, Ilker Demirel, Piersilvio De Bartolomeis +4
In many scientific experiments, the data annotating cost constraints the pace for testing novel hypotheses. Yet, modern machine learning pipelines offer a promising solution, provi…
The Narcissus Hypothesis: Descending to the Rung of Illusion
Riccardo Cadei, Christian Internò
Modern foundational models increasingly reflect not just world knowledge, but patterns of human preference embedded in their training data. We hypothesize that recursive alignment-…
Unifying Causal Representation Learning with the Invariance Principle
Dingling Yao, Dario Rancati, Riccardo Cadei +2
Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect o…