For example, if the initial study was an RCT, it may have failed to measure practically important Many applications of causal inference require covariates [Kahan et al., 2014] such as social using treatment effects estimated on a study determinants of health [Huang et al., 2024]. Since the population to make decisions in a separate intervention has not previously been used by the health target population. We consider the challenging system, no outcome data linked to these new covariates setting where there are covariates that are is available. However, treatment decisions would observed in the target population that were ideally reflect whether the intervention is likely to be not seen in the original study. Our goal is to beneficial to a patient conditional on all information estimate the tightest possible bounds on heterogeneous available, not just covariates that happened to be in the treatment effects conditioned on original study. This paper studies the question: how such newly observed covariates. We introduce precisely can we identify treatment effects conditional a novel partial identification strategy based on such new covariates? If precise estimates are available, on ideas from ecological inference; the main the decision maker can proceed confidently with idea is that estimates of conditional treatment deployment. Conversely, if considerable uncertainty remains effects for the full covariate set must about an important subgroup, a decision maker marginalize correctly when restricted to only may exercise more caution or invest more resources in the covariates observed in both populations.

Automated Planning is one of the main research field of Artificial Intelligence since its beginnings. Research in Automated Planning aims at developing general reasoners (i.e., planners) capable of automatically solve complex problems. Broadly speaking, planners rely on a general model characterizing the possible states of the world and the actions that can be performed in order to change the status of the world. Given a model and an initial known state, the objective of a planner is to synthesize a set of actions needed to achieve a particular goal state. The classical approach to planning roughly corresponds to the description given above. The timeline-based approach is a particular planning paradigm capable of integrating causal and temporal reasoning within a unified solving process. This approach has been successfully applied in many real-world scenarios although a common interpretation of the related planning concepts is missing. Indeed, there are significant differences among the existing frameworks that apply this technique. Each framework relies on its own interpretation of timeline-based planning and therefore it is not easy to compare these systems. Thus, the objective of this work is to investigate the timeline-based approach to planning by addressing several aspects ranging from the semantics of the related planning concepts to the modeling and solving techniques. Specifically, the main contributions of this PhD work consist of: (i) the proposal of a formal characterization of the timeline-based approach capable of dealing with temporal uncertainty; (ii) the proposal of a hierarchical modeling and solving approach; (iii) the development of a general purpose framework for planning and execution with timelines; (iv) the validation{\dag}of this approach in real-world manufacturing scenarios.

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity.