Search Results for author:
Found 16 papers, 4 papers with code
Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements
Social science researchers often use text as the raw data in investigations: for instance, when investigating the effects of IMF policies on the development of countries under IMF programs, researchers typically encode structured descriptions of the programs using a time-consuming manual effort.
Can Large Language Models (or Humans) Disentangle Text?
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 in AI and causal inference literature.
Deep Learning With DAGs
As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships.
CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images
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.
Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities
Via simulation, we also explore the sensitivity of satellite image-based observational inference to image resolution and to misspecification of the image-associated confounder.
$ρ$-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders
We propose a new sensitivity analysis model that combines copulas and normalizing flows for causal inference under unobserved confounding.
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Observational studies of causal effects require adjustment for confounding factors.
Image-based Treatment Effect Heterogeneity
Randomized controlled trials (RCTs) are considered the gold standard for estimating the average treatment effect (ATE) of interventions.
To What Extent Do Disadvantaged Neighborhoods Mediate Social Assistance Dependency? Evidence from Sweden
In this paper, we build on the theory of cumulative disadvantage and examine whether the accumulated use of social assistance over the life course is associated with an increased risk of future social assistance recipiency.
Conceptualizing Treatment Leakage in Text-based Causal Inference
However, these methods rely on a critical assumption that there is no treatment leakage: that is, the text only contains information about the confounder and no information about treatment assignment.
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows
Thus, our article shows how c-GNFs further the use of deep learning and causal inference in AI for social good.
Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flows
Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE.
The International Monetary Funds intervention in education systems and its impact on childrens chances of completing school
Enabling children to acquire an education is one of the most effective means to reduce inequality, poverty, and ill-health globally.
Measuring poverty in India with machine learning and remote sensing
In this paper, we use deep learning to estimate living conditions in India.
The wealth of nations and the health of populations: A quasi-experimental design of the impact of sovereign debt crises on child mortality
The wealth of nations and the health of populations are intimately strongly associated, yet the extent to which economic prosperity (GDP per capita) causes improved health remains disputed.
Statistical modeling: the three cultures
The algorithmic modeling culture (AMC) refers to practices defining a machine-learning (ML) procedure that generates accurate predictions about an event of interest.
Causal Inference
Methodology
Computers and Society
