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Mathias Drton

7 papers hereh-index 476 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • last author5

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • stat.ME4
  • math.ST2
  • q-bio.PE1
same name
  • Mathias Drton — 11 papers, h 5
  • Mathias Drton — 8 papers, h 2
  • Mathias Drton — 4 papers, h 3
  • Mathias Drton — 3 papers, h 1
  • Mathias Drton — 1 paper
  • Mathias Drton — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20062026
most citedA Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV Sequence Data

40 citations · 47 across the 7 of their papers we have counts for

collaborators
Showing stat.MEShow all

4 papers · 1 filter

stat.ME2026

Cost-Aware Optimized Front-Door Experimental Design

Leopold Mareis, Mathias Drton

Causal effect estimation often succeeds cost-constrained sequential data collection. This work considers multivariate linear front-door models with arbitrary unobserved confounding…

stat.ME2024

Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity

David Strieder, Mathias Drton

A fundamental challenge of scientific research is inferring causal relations based on observed data. One commonly used approach involves utilizing structural causal models that pos…

stat.ME2024

Dual Likelihood for Causal Inference under Structure Uncertainty

David Strieder, Mathias Drton

Knowledge of the underlying causal relations is essential for inferring the effect of interventions in complex systems. In a widely studied approach, structural causal models postu…

stat.ME2023★ 7 cited

Confidence in Causal Inference under Structure Uncertainty in Linear Causal Models with Equal Variances

David Strieder, Mathias Drton

Inferring the effect of interventions within complex systems is a fundamental problem of statistics. A widely studied approach employs structural causal models that postulate noisy…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.