11 citations · 12 across the 4 of their papers we have counts for
7 papers · 1 filter
The INSIDE assumption under all positive coding: interpretation and partial empirical assessment
Fernando Pires Hartwig, George Davey Smith, Frank Dudbridge +1
Mendelian randomisation (MR) implemented through instrumental variable (IV) analysis is a popular strategy for strengthening causal inference in observational studies. A key assump…
Towards more plausible point-identifying assumptions in two-sample Mendelian randomization
Fernando Pires Hartwig, Neil Martin Davies, George Davey Smith
Two-sample Mendelian randomization (MR) is a widely used methodology in epidemiology. In two-sample MR, summary data (typically regression coefficients and standard errors) quantif…
D-separation for applied researchers: understanding how to interpret directed acyclic graphs
Fernando Pires Hartwig, Timothy Feeney, Neil Davies
The assumed causal relationships depicted in a DAG are interpreted using a set of rules called D-separation rules. Although these rules can be implemented automatically using stand…
Empirically assessing the plausibility of unconfoundedness in observational studies
Fernando Pires Hartwig, Kate Tilling, George Davey Smith
The possibility of unmeasured confounding is one of the main limitations for causal inference from observational studies. There are different methods for (partially) empirically as…
A generalized definition of the average causal effect for both binary and continuous treatments
Fernando Pires Hartwig
One of the main tasks of causal inference is estimating well-defined causal parameters. One of the main causal parameters is the average causal effect (ACE) - the expected value of…
Homogeneity in the instrument-treatment association is not sufficient for the Wald estimand to equal the average causal effect for a binary instrument and a continuous exposure
Fernando Pires Hartwig, Linbo Wang, George Davey Smith +1
Background: Interpreting instrumental variable results often requires further assumptions in addition to the core assumptions of relevance, independence, and the exclusion restrict…
Average causal effect estimation via instrumental variables: the no simultaneous heterogeneity assumption
F. P. Hartwig, L. Wang, G. Davey Smith +1
Background: Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X has a causal effect on an outcome Y. Even if the instrument Z satisfies the thr…