Avi Feller
Biographic Data
| ID | 1900109 |
|---|---|
| NAME | Avi Feller |
| GIVEN NAMES | Avi |
| FAMILY NAME | Feller |
| SIGNATURE | FELLER A |
| AFFILIATIONS | University of California, Berkeley |
| ORCID | 0000-0001-7319-5468 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 28 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 3 |
Using Multiple Outcomes to Improve the Synthetic Control Method
When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome. In this paper, we instead propose estimating a common set of weights across outcomes, by balancing either a vector of all outcomes or an index or average of them. Under a low-rank factor model, we show that these approaches lead to lower bias bounds than separate weights, and that averaging leads to fur…
Multilevel Calibration Weighting for Survey Data
In the November 2016 U.S. presidential election, many state-level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the corresponding bias—and that these methods failed to adjust for important interactions between key var…
The Augmented Synthetic Control Method
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The “synthetic control” is a weighted average of control units that balances the treated unit’s pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an ex…
Information, Knowledge, and Attitudes
To better understand the relationship between information and political knowledge, we evaluate an ambitious government initiative: the nationwide dissemination of “taxpayer receipts,” or personalized, itemized accounts of government spending, by the UK government in fall 2014. In coordination with the British tax authorities, we embedded a survey experiment in a nationally representative panel. We find that citizens became more knowledgeable abou…
Reducing student absences at scale by targeting parents' misbeliefs
Algorithmic Decision Making and the Cost of Fairness
Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are substantially more likely than white defendants to be incorrectly classified as high risk. To mitigate such disparities, several techniques have recently been proposed to achieve algorithmic fairness. Here we reformulate algorithmic fairness as constrained optimization: the obj…
Hierarchical Models for Causal Effects
Hierarchical models play three important roles in modeling causal effects: (i) accounting for data collection, such as in stratified and split‐plot experimental designs; (ii) adjusting for unmeasured covariates, such as in panel studies; and (iii) capturing treatment effect variation, such as in subgroup analyses. Across all three areas, hierarchical models, especially Bayesian hierarchical modeling, offer substantial benefits over classical, non…
Principal Stratification
Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous subgroups, or principal strata, based on post-randomization behaviors under both the observed and the coun…
Red State/Blue State Divisions in the 2012 Presidential Election
The so-called “red/blue paradox” is that rich individuals are more likely to vote Republican but rich states are more likely to support the Democrats. Previous research argued that this seeming paradox could be explained by comparing rich and poor voters within each state – the difference in the Republican vote share between rich and poor voters was much larger in low-income, conservative, middle-American states like Mississippi than in high-inco…
Reducing student absences at scale by targeting parents' misbeliefs
Information, Knowledge, and Attitudes
To better understand the relationship between information and political knowledge, we evaluate an ambitious government initiative: the nationwide dissemination of “taxpayer receipts,” or personalized, itemized accounts of government spending, by the UK government in fall 2014. In coordination with the British tax authorities, we embedded a survey experiment in a nationally representative panel. We find that citizens became more knowledgeable abou…
Multilevel Calibration Weighting for Survey Data
In the November 2016 U.S. presidential election, many state-level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the corresponding bias—and that these methods failed to adjust for important interactions between key var…
Principal Stratification
Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous subgroups, or principal strata, based on post-randomization behaviors under both the observed and the coun…
Red State/Blue State Divisions in the 2012 Presidential Election
The so-called “red/blue paradox” is that rich individuals are more likely to vote Republican but rich states are more likely to support the Democrats. Previous research argued that this seeming paradox could be explained by comparing rich and poor voters within each state – the difference in the Republican vote share between rich and poor voters was much larger in low-income, conservative, middle-American states like Mississippi than in high-inco…
Hierarchical Models for Causal Effects
Hierarchical models play three important roles in modeling causal effects: (i) accounting for data collection, such as in stratified and split‐plot experimental designs; (ii) adjusting for unmeasured covariates, such as in panel studies; and (iii) capturing treatment effect variation, such as in subgroup analyses. Across all three areas, hierarchical models, especially Bayesian hierarchical modeling, offer substantial benefits over classical, non…
Principal Stratification
Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous subgroups, or principal strata, based on post-randomization behaviors under both the observed and the coun…
Algorithmic Decision Making and the Cost of Fairness
Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are substantially more likely than white defendants to be incorrectly classified as high risk. To mitigate such disparities, several techniques have recently been proposed to achieve algorithmic fairness. Here we reformulate algorithmic fairness as constrained optimization: the obj…
Information, Knowledge, and Attitudes
To better understand the relationship between information and political knowledge, we evaluate an ambitious government initiative: the nationwide dissemination of “taxpayer receipts,” or personalized, itemized accounts of government spending, by the UK government in fall 2014. In coordination with the British tax authorities, we embedded a survey experiment in a nationally representative panel. We find that citizens became more knowledgeable abou…
Reducing student absences at scale by targeting parents' misbeliefs
The Augmented Synthetic Control Method
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The “synthetic control” is a weighted average of control units that balances the treated unit’s pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an ex…
Multilevel Calibration Weighting for Survey Data
In the November 2016 U.S. presidential election, many state-level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the corresponding bias—and that these methods failed to adjust for important interactions between key var…
Using Multiple Outcomes to Improve the Synthetic Control Method
When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome. In this paper, we instead propose estimating a common set of weights across outcomes, by balancing either a vector of all outcomes or an index or average of them. Under a low-rank factor model, we show that these approaches lead to lower bias bounds than separate weights, and that averaging leads to fur…
Computer Science (6 works) · Econometrics (5 works) · Mathematics (5 works) · Economics (4 works) · Political science (4 works) · Advanced Causal Inference Techniques (3 works) · Artificial Intelligence (3 works) · Electoral Systems and Political Participation (3 works) · Statistical Methods and Bayesian Inference (3 works) · Statistics (3 works)