Using a simulation and two replication studies, they demonstrate that this approach adheres to the compositional data constraints and offers a more accurate interpretation of estimated treatment effects for proportional outcomes. You can read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Estimating Treatment Effects on Proportions with Synthetic Controls,” @bogatyrev.bsky.social and @lstoetze.bsky.social examine synthetic control methods (SCMs) and make the case for jointly estimating synthetic controls across multiple compositional outcomes.
Currently in FirstView: In “Text as Behavior,” @owasow.bsky.social proposes using features of open-ended tasks to study text as behavior. Stats like the number of characters can approximate effort and significantly improve estimation.
Currently in FirstView: In “Improving Small-Area Estimates of Public Opinion by Calibrating to Known Population Quantities,”
@wpmarble.bsky.social and Josh Clinton provide a framework for incorporating known population data to improve estimates of small subgroups in MRP models.
Currently in FirstView: In “Using Multilingual Language Technology to Classify Open-Ended Survey Responses: Conceptions of Democracy in a Cross-Cultural Survey Setting,” S. Dahlberg, L. Dürlich, S. Axelsson, Y. Zhao, and J. Nivre examine the use of LMs in analyzing open-ended survey responses.
Currently in FirstView: In “Democracy Manifest or Democracy Latent? A Unified Framework for Identifying Regime Types and Transitions,” @omerorsun.bsky.social and Muhammet A. Bas
develop and validate a framework to study regimes that addresses measurement uncertainty and missing data.
The paper walks through three studies where text is used to quantify attitudes and actions. Ultimately, the paper argues that expression is sufficiently demanding that it should be understood as a form of action. Read the paper here: www.cambridge.org/core/journal...
They validate this method using pre-election polling from the 2022 Michigan midterm and find that their calibrated MRP estimates reduce error by as much as two-thirds. You can read the full paper here: www.cambridge.org/core/journal...
The authors introduce a methodology that integrates LMs and structured coding schemes to classify open-ended survey responses cost-effectively and find that LMs can capture democratic perceptions and handle data abstractions. Read the full paper here: www.cambridge.org/core/journal...
Estimating Treatment Effects on Proportions with Synthetic Controls
www.cambridge.org
Their framework, UNITAS, reduces the dependence of inferences on specific datasets, cut-offs, magnitude-of-change and time-window assumptions, while efficiently handling missingness and measurement uncertainty. You can read the paper here: www.cambridge.org/core/journal...
