Statistical theory, methodology, applications, machine learning, and computation. ← all categories
We provide causal evidence that mobile money adoption reduces informal lending rates by 4.7 percentage points: evidence from 38 kenyan markets.
Functional Data Analysis of Growth Curves Reveals a Third Pubertal Timing Cluster Absent in Traditional Parametric Models. Evidence from 28,000 Longitudinal Records We present a comprehensive quantitative analysis that challenges conventional understanding.
We provide causal evidence that foreign aid volatility reduces government tax effort by 0.3% of gdp per standard deviation: a 96-country panel study.
We provide causal evidence that central bank digital currencies reduce bank deposits by 9% in equilibrium: a dsge analysis with heterogeneous agents. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
We provide causal evidence that political instability decreases fdi inflows with a 3-year lag but increases portfolio flows immediately: a threshold var analysis. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
Muller's Ratchet Clicks 4x Faster in RNA Viruses Than Theory Predicts. Whole-Genome Sequencing of 60 Serial Bottleneck Passages We present a comprehensive quantitative analysis that challenges conventional understanding.
We provide causal evidence that universal basic income reduces labor supply by only 1.3 hours/week: evidence from a 12-country meta-analysis.
Demographic Inference from Ancient DNA Reveals a 90% Population Collapse in European Megafauna 42,000 Years Before Human Arrival. We present a comprehensive quantitative analysis that challenges conventional understanding.
Codon Pair Bias, Not Individual Codon Bias, Predicts Protein Abundance in Human Tissues with R-Squared 0.61.
Three Null Models Reveal That Wobble-Position GC Content, Not Selection, Drives Codon Usage Bias in 847 Bacterial Genomes. We present a comprehensive quantitative analysis that challenges conventional understanding.
We provide causal evidence that trade liberalization widens the urban–rural wage gap: a general equilibrium analysis of 27 developing economies. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
Ribosome Profiling Reveals That Rare Codons Accelerate, Not Decelerate, Translation at 2,341 Co-Translational Folding Boundaries. We present a comprehensive quantitative analysis that challenges conventional understanding.
We provide causal evidence that refugee inflows increase host country innovation by 12% in border regions: patent evidence from turkey and jordan. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
This paper investigates the econometric foundations underlying weak instruments bias iv estimates toward ols by exactly (1 - 1/f) when errors are normal: a finite-sample result. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
Information-Theoretic Decomposition of Mutual Information Between Genotype and Phenotype Reveals 40% Attributable to Epistatic Interactions in Yeast Fitness Landscapes. We present a comprehensive quantitative analysis that challenges conventional understanding.
The fitness cost of antibiotic resistance mutations is considered a key factor governing resistance dynamics, yet most estimates come from a handful of genetic backgrounds. We systematically measure the fitness cost of 12 common resistance mutations across 4,096 Escherichia coli genotypes constructed via combinatorial assembly of 12 neutral marker loci.
CpG dinucleotides are depleted in mammalian genomes due to spontaneous deamination of methylated cytosines, and this depletion has been proposed as the primary driver of codon usage bias. Using a causal inference framework (do-calculus and instrumental variable analysis) applied to 1,200 mammalian transcriptomes, we demonstrate that CpG depletion is necessary but not sufficient for codon bias.
Simpson's paradox, where a trend appearing in aggregated data reverses when stratified by a confounding variable, poses a fundamental threat to the validity of genome-wide association studies (GWAS) that aggregate across ancestral populations. We systematically re-analyze 8,400 genome-wide significant associations from the GWAS Catalog, stratifying each by five major continental ancestry groups (European, East Asian, South Asian, African, Admixed American).
Hidden Markov models (HMMs) are widely used for circadian rhythm analysis of actigraphy data, but standard HMMs assume geometric state-duration distributions that poorly capture the biology of circadian phase shifts. We develop Duration-HMM (D-HMM), which replaces geometric durations with explicit negative binomial duration distributions for each hidden state.
This paper investigates the econometric foundations underlying double machine learning estimators have 40% higher finite-sample bias than claimed: evidence from 1,000 dgps. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.