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Andrew Liang

@andrewjliang.bsky.social

PoliSci-Data Analytics undergrad @ UCSD. Elections, causal inference, LLMs, and former (Oakland) Athletics fan. he/him.

30 Posts121 Follower493 Folgt
Robert Reich@rbreich.bsky.social · 4mo ago

The Texas legislature rammed through a gerrymandering bill at the request of Trump. Virginia actually voted in favor of redistricting to counterbalance what Texas and other Republican-controlled states have done. Yet only one is considered a "power grab." Oligarch-owned media in a nutshell.

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John Holbein@johnholbein1.bsky.social · 9mo ago

This is terrifying. "[AI agents] can... infer a researcher's latent hypotheses and produce data that artificially confirms them." ... "We can no longer trust that survey responses are coming from real people" -@seanjwestwood.bsky.social

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Don Moynihan@donmoyn.bsky.social · 9mo ago

New, from me: DHS says immigration enforcement employees like ICE are facing unprecedented threats and 1000% increase in assaults. But the data does not add up. Look at the details, and we see state agents using excessive force, and then lying about it. donmoynihan.substack.com/p/whos-threa...

Who's Threatening Who?

The Trump administration says immigration enforcement are being assaulted; the details tell a different story

donmoynihan.substack.com

Andrew Heiss@andrew.heiss.phd · 11mo ago

Just posted an updated/revised version of this “Statistical Methods in Public Policy Research” chapter, now under review post-R&R 🤞 I'm kinda partial and unbiased here, but I really really like this piece! HTML/PDF: stats.andrewheiss.com/snoopy-spring/ SocArXiv: doi.org/10.31235/osf...

Statistical Methods in Public Policy Research Chapter for the Oxford Research Encyclopedia on Public Policy This essay provides an overview of statistical methods in public policy, focused primarily on the United States. The essay traces the historical development of quantitative approaches in policy research, from early ad hoc applications through the 19th and early 20th centuries, to the full institutionalization of statistical analysis in federal, state, local, and nonprofit agencies by the late 20th century. It then outlines three core methodological approaches to policy-centered statistical research across social science disciplines: description, explanation, and prediction. In descriptive work, researchers explore what exists and examine any variable of interest to understand their different distributions and relationships. In explanatory work, researchers ask why does it exist and how can it be influenced. The focus of the analysis is on explanatory variables (X) to either (1) accurately estimate their relationship with an outcome variable (Y), or (2) causally attribute the effect of specific explanatory variables on outcomes. In predictive work, researchers ask what will happen next and focus on the outcome variable (Y) and on generating accurate forecasts, classifications, and predictions from new data. For each approach, the essay examines key techniques, their applications in policy contexts, and important methodological considerations. The discussion then considers critical perspectives on quantitative policy analysis framed around issues related to a three-part “data imperative” where governments are driven to count, gather, and learn from data. Each of these imperatives entail substantial issues related to privacy, accountability, democratic participation, and epistemic inequalities—issues at odds with public sector values of transparency and openness. The conclusion identifies some emerging trends in public sector-focused data science, inclusive ethi…Table of contents Introduction 1 Brief History of Statistics in Public Policy 2 Core Methodological Approaches 2.1 Description 2.2 Explanation 2.2.1 Estimation, Inference, and Hypothesis Testing 2.2.2 Causal Attribution and Causal Inference 2.3 Prediction 3 The Pitfalls of Counting, Gathering, and Learning from Public Data 4 Future Directions Further Reading References
Andrew Heiss@andrew.heiss.phd · last yr.

New preprint! A general overview of stats in public policy research with this (oversimplified but still helpful) separation of methods into description, explanation, and prediction #policysky HTML/PDF: stats.andrewheiss.com/snoopy-spring/ SocArXiv: doi.org/10.31235/osf...

This essay provides an overview of statistical methods in public policy, focused primarily on the United States. I trace the historical development of quantitative approaches in policy research, from early ad hoc applications through the 19th and early 20th centuries, to the full institutionalization of statistical analysis in federal, state, local, and nonprofit agencies by the late 20th century. I then outline three core methodological approaches to policy-centered statistical research across social science disciplines: description, explanation, and prediction, framing each in terms of the focus of the analysis. In descriptive work, researchers explore what exists and examine any variable of interest to understand their different distributions and relationships. In explanatory work, researchers ask why does it exist and how can it be influenced. The focus of the analysis is on explanatory variables (X) to either (1) accurately estimate their relationship with an outcome variable (Y), or (2) causally attribute the effect of specific explanatory variables on outcomes. In predictive work, researchers as what will happen next and focus on the outcome variable (Y) and on generating accurate forecasts, classifications, and predictions from new data. For each approach, I examine key techniques, their applications in policy contexts, and important methodological considerations. I then consider critical perspectives on quantitative policy analysis framed around issues related to a three-part “data imperative” where governments are driven to count, gather, and learn from data. Each of these imperatives entail substantial issues related to privacy, accountability, democratic participation, and epistemic inequalities—issues at odds with public sector values of transparency and openness. I conclude by identifying some emerging trends in public sector-focused data science, inclusive ethical guidelines, open research practices, and future directions for the field.
Ned Resnikoff@resnikoff.bsky.social · 11mo ago

If Newsom goes back on his promise here it may end up being one of the defining moments of his time as governor. Just an unmitigated disaster. www.sfchronicle.com/politics/art...

Massive Bay Area transit cuts likely as Newsom backtracks on $750M loan, lawmakers say

Bay Area lawmakers and transit advocates say Gov. Gavin Newsom’s reneging on a critical $750 million loan will have devastating consequences for BART, Muni and other agencies in the region.

sfchronicle.com

The New York Times@nytimes.com · last yr.

Erika McEntarfer led the federal agency that produced key data on jobs and inflation. Then President Trump lashed out over the agency’s most recent jobs report and fired her for releasing monthly data showing weak hiring. He called the data “rigged” without offering any evidence.

Until Trump Fired Her, She Was an Economist With Bipartisan Support

Erika McEntarfer led the agency that produced key data on jobs and inflation. Then July’s report showed a weakening economy, and President Trump accused her of “rigging” the numbers.

nyti.ms