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Tag: topic/uncertainty-quantification
13 items with this tag.
Sep 23, 2026
Q: What changes in the ROAS / mROAS optimization if the outcome is customer lifetime value rather than sales, given that CLV is a model-based forecast observed with delay and censoring?
type/qa
topic/customer-lifetime-value
topic/market-response
topic/causal-inference
topic/forecasting
topic/uncertainty-quantification
Sep 23, 2026
Q: S-, T-, X- and R-learners, causal forests / GRF, BART and Bayesian causal forests, moderation analysis, hierarchical models and conformal ITE intervals all address heterogeneous treatment effects. Which question does each actually answer, and what does its uncertainty interval mean?
type/qa
topic/treatment-effects
topic/causal-inference
topic/machine-learning
topic/uncertainty-quantification
topic/conformal-prediction
Sep 23, 2026
Conformal Prediction - Index
type/index
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
Sep 18, 2026
Q: Honest DiD, Rosenbaum-style sensitivity analysis for unobserved confounding, Plausible GMM's prior over moment misspecification, synthetic-control placebo and backdating checks, prior/likelihood power-scaling sensitivity and Sobol global sensitivity indices all ask how wrong the assumptions can be before the conclusion changes. What would a unified view look like?
type/qa
topic/causal-inference
topic/econometrics
topic/bayesian-workflow
topic/agent-based-modeling
topic/uncertainty-quantification
Sep 18, 2026
Q: Chinchilla compute-optimal training, optimal media mix under saturating response (ROAS / mROAS, Dorfman–Steiner), and power analysis / experimental design are all budget-allocation problems under diminishing returns. What transfers between them, and what does not?
type/qa
topic/market-response
topic/large-language-models
topic/bayesian-experimental-design
topic/uncertainty-quantification
Sep 18, 2026
Q: The vault uses 'calibration' in at least four senses — simulation-based calibration of a Bayesian computation, coverage calibration in conformal prediction, probabilistic calibration of forecasts under proper scoring rules, and parameter calibration of agent-based models (plus uncertainty calibration of probabilistic numerical solvers and LLM-population calibration). What does each one guarantee, and what does it not?
type/qa
topic/calibration
topic/bayesian-workflow
topic/conformal-prediction
topic/uncertainty-quantification
topic/agent-based-modeling
Sep 18, 2026
Conformal Inference for Counterfactuals and ITEs
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/causal-inference
topic/uncertainty-quantification
type/application
doc/paper
Sep 18, 2026
Conformal Prediction - Overview
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
type/overview
doc/paper
Sep 18, 2026
Conformal Prediction Under Covariate Shift
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
topic/distribution-shift
type/method
doc/paper
Sep 18, 2026
Conformalized Quantile Regression
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
topic/regression
type/method
doc/paper
Sep 18, 2026
Marginal vs Conditional Coverage
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
type/concept
doc/paper
Sep 18, 2026
Split Conformal Prediction and the Coverage Guarantee
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
type/method
doc/paper
Sep 18, 2026
Conformity Scores and Adaptive Prediction Sets
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
type/concept
doc/paper