Emily Aiken
Dados Biográficos
| ID | 6447352 |
|---|---|
| NOME | Emily Aiken |
| PRENOMES | Emily |
| SOBRENOME | Aiken |
| ASSINATURA | AIKEN E |
| AFILIAÇÕES | University of California, Berkeley |
| ORCID | 0000-0003-4374-3536 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAÇÕES | 4 |
| TOTAL COMO AUTOR | 3 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2019 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 1 |
Estimating impact with surveys versus digital traces
We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo, endline survey data indicate positive treatment effects on food security, mental health, and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict base…
Program targeting with machine learning and mobile phone data
Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor hou…
Neuroimaging Correlates of Suicidality in Decision-Making Circuits in Posttraumatic Stress Disorder
In depression, brain and behavioral correlates of decision-making differ between individuals with and without suicidal thoughts and behaviors. Though promising, it remains unknown if these potential biomarkers of suicidality will generalize to other high-risk clinical populations. To preliminarily assess whether brain structure or function tracked suicidality in individuals with posttraumatic stress disorder (PTSD), we measured resting-state func…
Program targeting with machine learning and mobile phone data
Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor hou…
Estimating impact with surveys versus digital traces
We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo, endline survey data indicate positive treatment effects on food security, mental health, and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict base…
Neuroimaging Correlates of Suicidality in Decision-Making Circuits in Posttraumatic Stress Disorder
In depression, brain and behavioral correlates of decision-making differ between individuals with and without suicidal thoughts and behaviors. Though promising, it remains unknown if these potential biomarkers of suicidality will generalize to other high-risk clinical populations. To preliminarily assess whether brain structure or function tracked suicidality in individuals with posttraumatic stress disorder (PTSD), we measured resting-state func…
Program targeting with machine learning and mobile phone data
Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor hou…
Estimating impact with surveys versus digital traces
We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo, endline survey data indicate positive treatment effects on food security, mental health, and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict base…
Economic growth (2 obras) · Economics (2 obras) · Income, Poverty, and Inequality (2 obras) · Mathematics (2 obras) · Poverty (2 obras) · Psychology (2 obras) · Statistics (2 obras) · Anterior cingulate cortex (1 obras) · Cash (1 obras) · Cash transfers (1 obras)