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98 lines (98 loc) · 1.75 KB
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terms
epidemiological-research-goals
potential-outcome
parameters-of-interest
te
ate
interpretation-of-ate
identifiability-assumptions
att
interpretation-of-att
att-vs.-ate
balance
balance-1
measures-of-balance
smd
variance-ratio
adjustment
why-adjust
adjustment-methods
lack-of-overlap
ps
motivating-problem
defining-propensity-score
theoretical-result
assumptions
ways-to-use-ps
ps-matching-steps
s1
model-specification
updating-model-specification
interactions
polynomial-terms
more-complex-functions
stability-of-ps
variables-to-adjust
best-approach
general-guideline-of-type-of-variables
what-not-to-include
mediators
unmeasured-confounding
model-selection
based-on-association-with-outcome
based-on-association-with-exposure
alternative-modelling-strategies
ps-estimation
s2
matching-method-nn
initial-fit
fine-tuning-add-caliper
things-to-keep-track-of
matches
other-matching-algorithms
s3
assessment-of-balance-by-smd
smd-vs.-p-values
vizualization-for-overlap
variance-ratio-1
close-inspection-of-boundaries
unsatirfactory-balance
s4
crude-outcome-model
double-adjustment
adjusted-outcome-model
variance-considerations
cluster-option
bootstrap
estimate-obtained
compare
data-simulation
treatment-effect-from-counterfactuals
treatment-effect-from-regression
treatment-effect-from-ps
non-linear-model
data-generation
regression
ps-1
machine-learning
regression-is-doomed
misspecify
complex-data-simulation
understanding-finite-sample-bias
estimation-using-different-methods
regression-1
propensity-score
double-machine-learning-method
augmented-inverse-probability-weighting
double-robust-method-tmle
guide
discipline-specific-reviews
suggested-guidelines
additional-topics
final
common-misconception
benifits-of-ps
limitations-of-ps
when-ps-may-not-be-useful
software
further-resources