In this paper, we focus on estimating the causal effect of an intervention over time on a dynamical system. 
To that end, we formally define causal interventions and their effects over time on discrete-time stochastic processes (DSPs). 
Then, we show under which conditions the equilibrium states of a DSP, both before and after a causal intervention, can be captured by a structural causal model (SCM). 
With such an equivalence at hand, we provide an explicit mapping from vector autoregressive models (VARs), broadly applied in econometrics, to linear, but potentially cyclic and/or affected by unmeasured confounders, SCMs. The resulting \emph{causal VAR framework} allows us to perform \emph{causal inference over time} from observational time series data.  
Our experiments on synthetic and real-world datasets show that the proposed framework achieves strong performance in terms of observational forecasting while enabling accurate estimation of the causal effect of interventions on dynamical systems. 
We demonstrate, through a case study, the potential practical questions that can be addressed using the proposed causal VAR framework.

A Practical Approach to Causal Inference over Time

This paper surveys Machine Learning approaches to build predictive models that know what they don't know. The consequential action of this knowledge can consist of abstaining from providing an output (rejection), deferring to another model (dynamic model selection), deferring to a human expert (learning to defer), or informing the user (uncertainty estimation). We formally state the problems each approach solves and point to key references. We discuss open issues that deserve investigation from the scientific community.

Things Machine Learning Models Know That They Don’t Know

There is a fast-growing literature in addressing the fairness of
AI models (fair-AI), with a continuous stream of new conceptual
frameworks, methods, and tools. How much can we trust
them? How much do they actually impact society? We take
a critical focus on fair-AI and survey issues, simplifications,
and mistakes that researchers and practitioners often underestimate,
which in turn can undermine the trust on fair-AI
and limit its contribution to society. In particular, we discuss
the hyper-focus on fairness metrics and on optimizing their
average performances. We instantiate this observation by discussing
the Yule’s effect of fair-AI tools: being fair on average
does not imply being fair in contexts that matter. We conclude
that the use of fair-AI methods should be complemented with
the design, development, and verification practices that are
commonly summarized under the umbrella of trustworthy AI.

Can We Trust Fair-AI?

Selective classification (also known as classification with reject option) conservatively extends a classifier with a selection function to determine whether or not a  prediction should be accepted (i.e., trusted, used, deployed). This is a highly relevant issue in socially sensitive tasks, such as credit scoring.
State-of-the-art approaches rely on Deep Neural Networks (DNNs) that train at the same time both the classifier and the selection function. These approaches are model-specific and computationally expensive. 
We propose a model-agnostic approach, as it can work with any base probabilistic binary classification algorithm, and it can be scalable to large tabular datasets if the base classifier is so. The proposed algorithm, called \textsc{SCross}, exploits a cross-fitting strategy and theoretical results for quantile estimation to build the selection function. Experiments on real-world data show that \textsc{SCross} improves over existing methods.

A Model-Agnostic Heuristics for Selective Classification

Knowledge Representation and Reasoning 8

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Senior Member Track Session 4

Senior Member Track Talks 3

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Salvatore Ruggieri

4

SHORT BIO

Presentations

A Practical Approach to Causal Inference over Time

Things Machine Learning Models Know That They Don’t Know

Can We Trust Fair-AI?

A Model-Agnostic Heuristics for Selective Classification

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