Adel Daoud
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View article: Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis
Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis Open
Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can be leve…
View article: Benchmarking Debiasing Methods for LLM-based Parameter Estimates
Benchmarking Debiasing Methods for LLM-based Parameter Estimates Open
Large language models (LLMs) offer an inexpensive yet powerful way to annotate text, but are often inconsistent when compared with experts. These errors can bias downstream estimates of population parameters such as regression coefficients…
View article: To what extent do disadvantaged neighbourhoods mediate social assistance dependency? Evidence from Sweden
To what extent do disadvantaged neighbourhoods mediate social assistance dependency? Evidence from Sweden Open
This article investigates social assistance dependency and its relation to neighbourhood disadvantage in Sweden. We combine Swedish register data, tracking and analysing a cohort from 1998–2017, with the help of causal mediation, our analy…
View article: A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty Open
Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer visi…
View article: A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty Open
Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer visi…
View article: Linking Socioeconomic Status and Emissions: The Predictive Power of the International Wealth Index for NO2 column densities
Linking Socioeconomic Status and Emissions: The Predictive Power of the International Wealth Index for NO2 column densities Open
Ensuring accountability in emissions reductions is critical as many nations struggle to meet their Nationally Determined Contributions (NDCs) under the Paris Agreement. Traditional greenhouse gas (GHG) monitoring approaches are hindered by…
View article: Mapping Africa Settlements: High Resolution Urban and Rural Map by Deep Learning and Satellite Imagery
Mapping Africa Settlements: High Resolution Urban and Rural Map by Deep Learning and Satellite Imagery Open
Accurate Land Use and Land Cover (LULC) maps are essential for understanding the drivers of sustainable development, in terms of its complex interrelationships between human activities and natural resources. However, existing LULC maps oft…
View article: Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach
Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach Open
Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs). Achieving this requires high-frequency, granular data to capture neighborhood-level changes, particularly in data scarce regions such as low- and middl…
View article: Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs
Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs Open
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 s…
View article: A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty Open
Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research in computer vision…
View article: A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty Open
Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer visi…
View article: A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty Open
Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer visi…
View article: Can Large Language Models (or Humans) Disentangle Text?
Can Large Language Models (or Humans) Disentangle Text? Open
We investigate the potential of large language models (LLMs) to disentangle text variables--to remove the textual traces of an undesired forbidden variable in a task sometimes known as text distillation and closely related to the fairness …
View article: Deep Learning with DAGs
Deep Learning with DAGs Open
Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in p…
View article: Deep Learning With DAGs
Deep Learning With DAGs Open
Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in p…
View article: Can Large Language Models (or Humans) Disentangle Text?
Can Large Language Models (or Humans) Disentangle Text? Open
We investigate the potential of large language models (LLMs) to disentangle text variables—to remove the textual traces of an undesired forbidden variable in a task sometimes known as text distillation and closely related to the fairness i…
View article: The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low- and middle-income countries
The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low- and middle-income countries Open
Which children are most vulnerable when their government imposes austerity? Research tends to focus on either the political-economic level or the family level. Using a sample of nearly two million children in 67 countries, this study synth…
View article: Navigating Unmeasured Confounding in Quantitative Sociology: A Sensitivity Framework
Navigating Unmeasured Confounding in Quantitative Sociology: A Sensitivity Framework Open
Unmeasured confounding remains a critical challenge in causal inference for the social sciences. This paper proposes a sensitivity analysis framework to systematically evaluate how unmeasured confounders influence statistical inference in …
View article: CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images
CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images Open
The causalimages R package enables causal inference with image and image sequence data, providing new tools for integrating novel data sources like satellite and bio-medical imagery into the study of cause and effect. One set of functions …
View article: Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa
Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa Open
To combat poor health and living conditions, policymakers in Africa require temporally and geographically granular data measuring economic well-being. Machine learning (ML) offers a promising alternative to expensive and time-consuming sur…
View article: Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities
Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities Open
Observational studies require adjustment for confounding factors that are correlated with both the treatment and outcome. In the setting where the observed variables are tabular quantities such as average income in a neighborhood, tools ha…
View article: Statistical Modeling: The Three Cultures
Statistical Modeling: The Three Cultures Open
Social scientists distinguish between predictive and causal research.While this distinction clarifies the aims of two research traditions, this clarity is blurred by the introduction of machine learning (ML) algorithms.Although ML excels i…
View article: Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach
Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach Open
This study examined heterogeneity in the association between disaster-related home loss and functional limitations of older adults, and identified characteristics of vulnerable subpopulations. Data were from a prospective cohort study of J…
View article: $ρ$-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows
$ρ$-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows Open
We propose a novel sensitivity analysis to unobserved confounding in observational studies using copulas and normalizing flows. Using the idea of interventional equivalence of structural causal models, we develop $ρ$-GNF ($ρ$-graphical nor…
View article: Long-Term Associations between Disaster-Related Home Loss and Health and Well-Being of Older Survivors: Nine Years after the 2011 Great East Japan Earthquake and Tsunami
Long-Term Associations between Disaster-Related Home Loss and Health and Well-Being of Older Survivors: Nine Years after the 2011 Great East Japan Earthquake and Tsunami Open
Home loss due to a disaster may have long-lasting adverse impacts on the cognitive social capital, mental health, and prosociality of older adult survivors. https://doi.org/10.1289/EHP10903.
View article: Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows
Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows Open
Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inve…
View article: Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa Open
Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well understood. However, in public p…
View article: Image-based Treatment Effect Heterogeneity
Image-based Treatment Effect Heterogeneity Open
Randomized controlled trials (RCTs) are considered the gold standard for estimating the average treatment effect (ATE) of interventions. One use of RCTs is to study the causes of global poverty -- a subject explicitly cited in the 2019 Nob…