Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs

• URL: http://arxiv.org/abs/2411.02134v2
• Date: Sat, 15 Mar 2025 19:26:10 GMT
• Title: Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs
• Authors: Fucheng Warren Zhu, Connor T. Jerzak, Adel Daoud,
• Abstract summary: Multi-Scale Representation Concatenation transforms arbitrary single-scale EO-based CATE estimation algorithms into multi-scale ones.<n>We benchmark the performance of Multi-Scale Representation Concatenation on a CATE estimation pipeline.<n>Results indicate that Multi-Scale Representation Concatenation improves the performance of deep learning models in EO-based CATE estimation.
• Score: 3.4137115855910762
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Earth Observation (EO) data are increasingly used in policy analysis by enabling granular estimation of conditional average treatment effects (CATE). However, a challenge in EO-based causal inference is determining the scale of the input satellite imagery -- balancing the trade-off between capturing fine-grained individual heterogeneity in smaller images and broader contextual information in larger ones. This paper introduces Multi-Scale Representation Concatenation, a set of composable procedures that transform arbitrary single-scale EO-based CATE estimation algorithms into multi-scale ones. We benchmark the performance of Multi-Scale Representation Concatenation on a CATE estimation pipeline that combines Vision Transformer (ViT) models (which encode images) with Causal Forests (CFs) to obtain CATE estimates from those encodings. We first perform simulation studies where the causal mechanism is known, showing that our multi-scale approach captures information relevant to effect heterogeneity that single-scale ViT models fail to capture as measured by $R^2$. We then apply the multi-scale method to two randomized controlled trials (RCTs) conducted in Peru and Uganda using Landsat satellite imagery. As we do not have access to ground truth CATEs in the RCT analysis, the Rank Average Treatment Effect Ratio (RATE Ratio) measure is employed to assess performance. Results indicate that Multi-Scale Representation Concatenation improves the performance of deep learning models in EO-based CATE estimation without the complexity of designing new multi-scale architectures for a specific use case. The application of Multi-Scale Representation Concatenation could have meaningful policy benefits -- e.g., potentially increasing the impact of poverty alleviation programs without additional resource expenditure.

Related papers

• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Estimation of individual causal effects in network setup for multiple treatments [4.53340898566495]
  We study the problem of estimation of Individual Treatment Effects (ITE) in the context of multiple treatments and observational data. We employ Graph Convolutional Networks (GCN) to learn a shared representation of the confounders. Our approach utilizes separate neural networks to infer potential outcomes for each treatment.
  arXiv  Detail & Related papers  (2023-12-18T06:07:45Z)
• RGM: A Robust Generalizable Matching Model [49.60975442871967]
  We propose a deep model for sparse and dense matching, termed RGM (Robust Generalist Matching) To narrow the gap between synthetic training samples and real-world scenarios, we build a new, large-scale dataset with sparse correspondence ground truth. We are able to mix up various dense and sparse matching datasets, significantly improving the training diversity.
  arXiv  Detail & Related papers  (2023-10-18T07:30:08Z)
• Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques [3.2422067155309806]
  We propose an ensemble technique that combines multiple base models toward the final decision. A semi-supervised approach using a Logistic Regressor to combine the base models' outputs is also proposed. The performance improvement in terms of anomaly detection accuracy reaches 2% for the unsupervised and at least 10% for the semi-supervised models.
  arXiv  Detail & Related papers  (2023-08-06T17:51:22Z)
• Revisiting the Evaluation of Image Synthesis with GANs [55.72247435112475]
  This study presents an empirical investigation into the evaluation of synthesis performance, with generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses of various factors, including how to represent a data point in the representation space, how to calculate a fair distance using selected samples, and how many instances to use from each set.
  arXiv  Detail & Related papers  (2023-04-04T17:54:32Z)
• ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold [13.379416816598873]
  This paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an off-line optimization problem. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise.
  arXiv  Detail & Related papers  (2022-11-30T09:55:45Z)
• U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold [0.40964539027092906]
  We propose a one-class self-supervised method for anomaly segmentation in images. It benefits from a modern machine learning approach and a more classic statistical detection theory. The proposed approach produces state-of-the-art results for all metrics and all datasets.
  arXiv  Detail & Related papers  (2022-11-22T15:43:19Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution [48.093500219958834]
  We propose an Accelerated Multi-Scale Aggregation network (AMSA) for Reference-based Super-Resolution. The proposed AMSA achieves superior performance over state-of-the-art approaches on both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2022-01-12T08:40:23Z)
• Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects [83.66276516095665]
  Conditional average treatment effects (CATEs) allow us to understand the effect heterogeneity across a large population of individuals. Typical CATE learners assume all confounding variables are measured in order for the CATE to be identifiable. We propose an energy-based model (EBM) that learns a low-dimensional representation of the variables by employing a noise contrastive loss function.
  arXiv  Detail & Related papers  (2021-08-06T10:39:49Z)
• Semantic Change Detection with Asymmetric Siamese Networks [71.28665116793138]
  Given two aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem is vital in many earth vision related tasks, such as precise urban planning and natural resource management. We present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures.
  arXiv  Detail & Related papers  (2020-10-12T13:26:30Z)
• Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization [30.203072945001136]
  In this work, a general architecture is first formulated probabilistically to extract domain invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMUSeasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision.
  arXiv  Detail & Related papers  (2020-09-16T14:43:22Z)
• MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling [66.01558025094333]
  We propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss.
  arXiv  Detail & Related papers  (2020-05-15T10:37:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.