Research

You can also find my articles on my Google Scholar profile.

Causal Carbon: Baselines and Additionality with Potential Outcomes

Preprint

Recent work has questioned the credibility of forest carbon offsets as an environmental intervention and nature-based solution for mitigating climate change. Despite some updates to carbon credit methodologies and advice to purchase only high-integrity or high-quality credits, it is not clear which carbon offsets meet these standards under which conditions. In this paper, we draw on the fields of statistics and causal inference to develop a generalized framework for analyzing carbon offset protocols. We show that strategic enrollment combined with even seemingly innocuous measurement errors in carbon stocks can lead to market distortions and that there is an inherent tradeoff between minimizing these distortions and broadening enrollment. The provided framework clarifies what purchasers of carbon offsets must believe about the world in order for purchased credits under each protocol to accurately reflect the impact of crediting programs and builds common ground on which more fruitful engagement between different sectors of the carbon market can build agreement.

Recommended citation: Ayers, M., Sanford, L., Gardner, W., & Kuebbing, S. (2025, March 7). Causal Carbon: Baselines and Additionality with Potential Outcomes. https://doi.org/10.31219/osf.io/5pcuh_v2
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Adversarial Debiasing for Unbiased Parameter Recovery

Preprint

Advances in machine learning and the increasing availability of high-dimensional data have led to the proliferation of social science research that uses the predictions of machine learning models as proxies for measures of human activity or environmental outcomes. However, prediction errors from machine learning models can lead to bias in the estimates of regression coefficients. In this paper, we show how this bias can arise, propose a test for detecting bias, and demonstrate the use of an adversarial machine learning algorithm in order to de-bias predictions. These methods are applicable to any setting where machine-learned predictions are the dependent variable in a regression. We conduct simulations and empirical exercises using ground truth and satellite data on forest cover in Africa. Using the predictions from a naive machine learning model leads to biased parameter estimates, while the predictions from the adversarial model recover the true coefficients.

Recommended citation: Sanford, L., Ayers, M., Gordon, M., & Stone, E. (2025). Adversarial Debiasing for Unbiased Parameter Recovery. https://www.arxiv.org/abs/2502.12323
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Discovering Influential Text Using Convolutional Neural Networks

Findings of the Association for Computational Linguistics ACL, 2024

Experimental methods for estimating the impacts of text on human evaluation have been widely used in the social sciences. However, researchers in experimental settings are usually limited to testing a small number of pre-specified text treatments. While efforts to mine unstructured texts for features that causally affect outcomes have been ongoing in recent years, these models have primarily focused on the topics or specific words of text, which may not always be the mechanism of the effect. We connect these efforts with NLP interpretability techniques and present a method for flexibly discovering clusters of similar text phrases that are predictive of human reactions to texts using convolutional neural networks. When used in an experimental setting, this method can identify text treatments and their effects under certain assumptions. We apply the method to two data sets. The first enables direct validation of the model’s ability to detect phrases known to cause the outcome. The second demonstrates its ability to flexibly discover text treatments with varying textual structures. In both cases, the model learns a greater variety of text treatments compared to benchmark methods, and these text features quantitatively meet or exceed the ability of benchmark methods to predict the outcome.

Recommended citation: Megan Ayers, Luke Sanford, Margaret Roberts, and Eddie Yang. 2024. Discovering influential text using convolutional neural networks. In Findings of the Association for Computational Linguistics ACL 2024, pages 12002–12027, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
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Changes in Global Warming’s Six Americas: An Analysis of Repeat Respondents

Climatic Change, 2024

Building public consensus about the threat of climate change is critical for enacting meaningful action to address it. To understand how Americans are changing their beliefs about climate change, research typically relies on cross-sectional survey responses. Data that is collected from the same individuals over time– panel data– provides clearer evidence about whether people’s beliefs are shifting. In this article, we investigate changes in climate beliefs among the American public using panel data from 2,135 survey respondents, analyzing opinion changes through the “Global Warming’s Six Americas” framework– an audience segmentation tool that identifies the people who are the most worried about global warming (the Alarmed) to the least worried (the Dismissive). Our findings indicate that many Americans are changing their minds about climate change and becoming more worried over time, and that these shifts correlate with changes in support for climate policy and behavioral engagement. However, these trends vary within key segments of the population and indicate that while climate communication may be shifting the beliefs of many, strategies for reaching particular audiences may need to be adapted.

Recommended citation: Ayers, M., Marlon, J.R., Ballew, M.T. et al. Changes in Global Warming’s Six Americas: an analysis of repeat respondents. Climatic Change 177, 96 (2024). https://doi.org/10.1007/s10584-024-03754-x
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