Bio | Papers {Substantive, Methodological} | Visualizations | Students
Present:
[1.] Assistant Professor in the Department of Government at the University of Texas at Austin.
[2.] Consultant, Institute for Health Metrics & Evaluation (IHME), University of Washington.
Past:
[1.] Visiting Assistant Professor in the Department of Government at Harvard University (2024).
[2.] Postdoc, AI & Global Development Lab (2021-2022).
Methodological work: AI and global development, EO for causal inference, adversarial dynamics, computational text analysis.
Substantive work: Political economy, social movements, descriptive representation.
[PlanetaryCausalInference.org]
[AI & Global Development Lab GitHub]
[YouTube Tutorials] [Data Assets]
| Cindy Conlin | Andrés Cruz |
| Cem Mert Dallı | Beniamino Green |
| SayedMorteza Malaekeh | Nicolas Audinet de Pieuchon |
| Kazuki Sakamoto | Ritwik Vashistha |
| Fucheng Warren Zhu |
- Nicolas Audinet de Pieuchon presents: Benchmarking Debiasing Methods for LLM-based Parameter Estimates
- Nicolas Audinet de Pieuchon presents: Can Large Language Models (or Humans) Disentangle Text?
- Adel Daoud presents: A First Course in Planetary Causal Inference: Confounding (@IC2S2 2025)
- Adel Daoud presents: Planetary Causal Inference: Overview (@Yale)
- Connor Jerzak presents: Seeing Like a Satellite While Learning Across Scales: Remote Audits + Multi-Scale Optimization for Heterogeneity (@Columbia)
- Connor Jerzak presents: Selecting Optimal Candidate Profiles in Adversarial Environments (@UT Dallas & National Chung Hsing University)
- Richard Johansson presents: Conceptualizing Treatment Leakage in Text-based Causal Inference (@NAACL)
- Satiyabooshan Murugaboopathy presents: Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?
- Kazuki Sakamoto presents: A Scoping Review of Earth Observation and Machine Learning for Causal Inference
- Fucheng Warren Zhu presents: Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using EO and Computer Vision
[Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis] [.bib]*
[Effect Heterogeneity with Earth Observation in Randomized Controlled Trials: Exploring the Role of Data, Model, and Evaluation Metric Choice] [.bib]*
[Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs] [Video] [.bib]*
[A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty] [.bib] [Data]*
[Image De-confounding] [.bib] [Code]
[Can Large Language Models (or Humans) Disentangle Text Features?] [.bib] [Code]*
[Image-based Treatment Effect Heterogeneity] [.bib] [Code]
[Non-parametric Content Analysis] [.bib] [Code]
[Linking Datasets on Organizations Using Half A Billion Open Collaborated Records] [.bib] [Code]
[Degrees of Randomness in Rerandomization Procedures] [.bib] [Code]
[Where Minorities are the Majority: Electoral Rules and Ethnic Representation] [.bib]
[The Composition of Descriptive Representation] [.bib] [Code]
[Housing Values and Partisanship: Evidence from E-ZPass] [.bib]
*indicates joint work with graduate student co-author(s). See [Students] for more information.
  Planetary Causal Inference Workflow |   Institutional Analysis |
  Fast Rerandomization with Accelerated Computing |   Effect Heterogeneity with Image Sequences |
  PSRM 2024 |   ACL Anthology |
  PCI Book Launch |