Pages that link to "Item:Q1595998"

The following pages link to Comment on J. Neyman and causal inference in experiments and observational studies: ''On the application of probability theory to agricultural experiments. Essay on principles. Section 9'' [Ann. Agric. Sci. 10 (1923), 1--51] (Q1595998):

Displaying 50 items.

• Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments (Q93156) (← links)
• A conversation with Donald B. Rubin (Q252737) (← links)
• Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score (Q257459) (← links)
• Comments on the Neyman-Fisher controversy and its consequences (Q257696) (← links)
• Causal inference through potential outcomes and principal stratification: application to studies with ``censoring'' due to death (Q449724) (← links)
• A Bayesian nonparametric causal model (Q665048) (← links)
• Ridge rerandomization: an experimental design strategy in the presence of covariate collinearity (Q826983) (← links)
• Conceptual, computational and inferential benefits of the missing data perspective in applied and theoretical statistical problems (Q878277) (← links)
• The essential role of pair matching in cluster-randomized experiments, with application to the Mexican Universal Health Insurance evaluation (Q900473) (← links)
• Balancing scores for simultaneous comparisons of multiple treatments (Q900946) (← links)
• Bias of the regression estimator for experiments using clustered random assignment (Q952852) (← links)
• For objective causal inference, design trumps analysis (Q958314) (← links)
• Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences (Q963906) (← links)
• Clinician preferences and the estimation of causal treatment differences. (With comments) (Q1400099) (← links)
• Confounding and collapsibility in causal inference (Q1431156) (← links)
• On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed (Q1596000) (← links)
• Introductory remarks on J. Neyman: ''On the application of probability theory to agricultural experiments. Essay on principles. Section 9''. (Q1596001) (← links)
• Covariate balancing propensity score for a continuous treatment: application to the efficacy of political advertisements (Q1647599) (← links)
• A paradox from randomization-based causal inference (Q1750243) (← links)
• Bipartite causal inference with interference (Q2038289) (← links)
• Monosynaptic inference via finely-timed spikes (Q2137313) (← links)
• Evidence factors in a case-control study with application to the effect of flexible sigmoidoscopy screening on colorectal cancer (Q2194469) (← links)
• A generalized approach to power analysis for local average treatment effects (Q2218045) (← links)
• Semi-supervised inference: general theory and estimation of means (Q2328051) (← links)
• Latent class dynamic mediation model with application to smoking cessation data (Q2331141) (← links)
• Estimating the causal effects of marketing interventions using propensity score methodology (Q2381782) (← links)
• A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs (Q2800208) (← links)
• Two Critical Issues in Quantitative Modeling of Communicable Diseases: Inference of Unobservables and Dependent Happening (Q2820309) (← links)
• The Intensity-Score Approach to Adjusting for Confounding (Q3079109) (← links)
• Generalized Causal Mediation Analysis (Q3100808) (← links)
• The broad role of multiple imputation in statistical science (Q3297916) (← links)
• Methods for clustered encouragement design studies with noncompliance and missing data (Q3303679) (← links)
• Bayesian inference for causal mediation effects using principal stratification with dichotomous mediators and outcomes (Q3305026) (← links)
• Identification of causal effects using instrumental variables in randomized trials with stochastic compliance (Q4902192) (← links)
• Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments (Q4962451) (← links)
• Causal Inference With Interference and Noncompliance in Two-Stage Randomized Experiments (Q4999138) (← links)
• Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects (Q5134472) (← links)
• Design, Identification, and Sensitivity Analysis for Patient Preference Trials (Q5208058) (← links)
• The Blessings of Multiple Causes (Q5208062) (← links)
• Comment: The Challenges of Multiple Causes (Q5208066) (← links)
• Causal Interaction in Factorial Experiments: Application to Conjoint Analysis (Q5231479) (← links)
• A Potential Outcomes Approach to Developmental Toxicity Analyses (Q5492072) (← links)
• Empirical Likelihood in Causal Inference (Q5864353) (← links)
• Causal inference: Critical developments, past and future (Q6059423) (← links)
• Sampling‐based Randomised Designs for Causal Inference under the Potential Outcomes Framework (Q6064341) (← links)
• Another Look at the Lady Tasting Tea and Differences Between Permutation Tests and Randomisation Tests (Q6066746) (← links)
• Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups (Q6079677) (← links)
• Instrumented Difference-in-Differences (Q6079738) (← links)
• Optimal Covariate Balancing Conditions in Propensity Score Estimation (Q6586890) (← links)
• Causal deep learning: encouraging impact on real-world problems through causality (Q6599136) (← links)