Publications
List of my publications in reversed chronological order.
NetworkCausalTree: an R package for estimating heterogeneous effects under interference [UNDER REVIEW]
Journal of Open Source Software
The NetworkCausalTree package introduces a machine learning method that uses tree-based algorithms and an Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects in clustered network interference. Causal inference studies typically assume no interference between individuals, but in real-world scenarios where individuals are interconnected through social, physical, or virtual ties, the effect of a treatment can spill over to other connected individuals in the network. To avoid biased estimates of treatment effects, interference should be considered. Understanding the heterogeneity of treatment and spillover effects can help policy-makers scale up interventions, target strategies more effectively, and generalize treatment spillover effects to other populations.
2024
Projecting the climate penalty on PM2.5 pollution with spatial deep learning (short)
Workshop in Tackling Climate Change with Machine Learning at ICLR, 2023
The climate penalty measures the effects of a changing climate on air quality due to the interaction of pollution with climate factors, independently of future changes in emissions. This work introduces a statistical framework for estimating the climate penalty on soot pollution (PM 2.5), which has been linked to respiratory and cardiovascular diseases and premature mortality. The framework is used to evaluate the disparities in future PM 2.5 exposure across racial/ethnic and income groups. The findings of this study have the potential to inform mitigation policy aiming to protect public health and promote environmental equity in addressing the effects of climate change. The proposed methodology significantly improves upon existing statistical-based methods for estimating the climate penalty. It will use higher-resolution climate inputs---which current statistical approaches cannot accommodate---using an expressive and scalable predictive model based on spatial deep learning with spatiotemporal trend estimation. It will also integrate additional predictive data sources such as demographics and geology. This approach allows us to consider regional dependencies and synoptic weather patterns that influence PM 2.5, and deconvolve them from the effects of exogenous factors, such as the trends in increasing air quality regulations and other sources of unmeasured spatial heterogeneity.CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects
Journal of Open Source Software
In health and social sciences, it is critically important to identify interpretable subgroups of the study population where a treatment has notable heterogeneity in the causal effects with respect to the average treatment effect (ATE). Several approaches have already been proposed for heterogenous treatment effect (HTE) discovery, either estimating first the conditional average treatment effect (CATE) and identifying heterogeneous subgroups in a second stage, either in a direct data-driven procedure. Many of these methodologies are decision tree-based methodologies. Tree-based approaches are based on efficient and easily implementable recursive mathematical programming (e.g., HTE maximization), they can be easily tweaked and adapted to different scenarios depending on the research question of interest, and they guarantee a high degree of interpretability---i.e., the degree to which a human can understand the cause of a decision. Despite these appealing features, single-tree heterogeneity discovery is characterized by two main limitations: instability in the identification of the subgroups and reduced exploration of the potential heterogeneity. To accommodate these shortcomings, Bargagli et al. (2023) proposed Causal Rule Ensemble, a new method for interpretable HTE characterization in terms of decision rules, via an extensive exploration of heterogeneity patterns by an ensemble-of-trees approach, enforcing high stability in the discovery. CRE is an R package providing a flexible implementation of Causal Rule Ensemble algorithm.Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects [UNDER REVIEW]
arXiv
In health and social sciences, it is critically important to identify subgroups of the study population where a treatment has notable heterogeneity in the causal effects with respect to the average treatment effect. Data-driven discovery of heterogeneous treatment effects (HTE) via decision tree methods has been proposed for this task. Despite its high interpretability, the single-tree discovery of HTE tends to be highly unstable and to find an oversimplified representation of treatment heterogeneity. To accommodate these shortcomings, we propose Causal Rule Ensemble (CRE), a new method to discover heterogeneous subgroups through an ensemble-of-trees approach. CRE has the following features: 1) provides an interpretable representation of the HTE; 2) allows extensive exploration of complex heterogeneity patterns; and 3) guarantees high stability in the discovery. The discovered subgroups are defined in terms of interpretable decision rules, and we develop a general two-stage approach for subgroup-specific conditional causal effects estimation, providing theoretical guarantees. Via simulations, we show that the CRE method has a strong discovery ability and a competitive estimation performance when compared to state-of-the-art techniques. Finally, we apply CRE to discover subgroups most vulnerable to the effects of exposure to air pollution on mortality for 35.3 million Medicare beneficiaries across the contiguous U.S.
2023
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective (long)
IEEE Conference on Computer Vision and Pattern Recognition, 2022
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under covariate shift and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables, namely invariant mechanisms, style confounders, and spurious features. We then introduce a learning framework that treats each group separately: (i) unlike the common practice of merging datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss encouraging the model to suppress spurious correlations; (ii) we devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph; (iii) we introduce a style consistency loss that not only enforces the structure of style representations but also serves as a self-supervisory signal for test-time refinement on the fly. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations, outperforming prior state-of-the-art motion forecasting models for out-of-distribution generalization and low-shot transfer.
2022
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective (short)
Workshop in Distribution Shifts at NeurIPS, 2021
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two fundamental shortcomings: brittle under covariate shift and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with physical mechanisms, style confounders, and spurious correlations. We then propose two components that explicitly promote the robustness and reusability of the learned motion representations: (i) unlike the common practice of merging datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss function, which encourages the model to suppress spurious correlations and capture physical mechanisms; (ii) we devise a modular architecture that factorizes the representations of physical laws and motion styles in a structured way, and progressively prune their dense connections during training to approximate a sparse causal graph. We empirically validate the strength of the proposed method for robust generalization in controlled real-world experiments. We finally discuss the challenges and opportunities in the presence of style shifts through synthetic simulations.Quantification of the available area for rooftop photovoltaic installation from overhead imagery using convolutional neural networks
Journal of Physics: Conference Series, 2021
The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we combine state-of-the-art Machine Learning and computer vision techniques together with high-resolution overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further associate them to the corresponding buildings by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the available area.