Пожертвування 15 вересня 2024 – 1 жовтня 2024 Про збір коштів

Causal inference

Main
Causal inference

Causal inference

Hernán M. A., Robins J. M.

0 / 0

Наскільки Вам сподобалась ця книга?

Яка якість завантаженого файлу?

Скачайте книгу, щоб оцінити її якість

Яка якість скачаних файлів?

Рік:

2019

Видання:

web draft

Видавництво:

Boca Raton: Chapman & Hall/CRC

Мова:

english

Сторінки:

284

Файл:

PDF, 3.24 MB

IPFS:

,

english, 2019

Читати Онлайн

Виконується конвертація в

Конвертація в не вдалась

Можливо вас зацікавить

Miguel A. Hernán, James M. Robins — Causal Inference: What If

Judea Pearl — An Introduction to Causal Inference

Hernan M.A., Robins J.M. — Cause and correlation in biology : a user's guide to path analysis, structural equations and causal inference

Alison Gopnik, Laura Schulz — Causal Learning - Psychology, Philosophy and Computation

Wesley C. Salmon — Causality and Explanation

Steven Sloman — Causal Models: How People Think about the World and Its Alternatives

Tyler VanderWeele — Explanation in Causal Inference: Methods for Mediation and Interaction

Phyllis McKay Illari, Federica Russo, Jon Williamson — Causality in the Sciences

Phyllis Illari, Federica Russo — Causality: Philosophical Theory Meets Scientific Practice

Richard J. Murnane, John B. Willett — Methods Matter: Improving Causal Inference in Educational and Social Science Research

Joseph Y. Halpern — Actual Causality

Jonas Peters, Dominik Janzing, Bernhard Schölkopf — Elements of Causal Inference: Foundations and Learning Algorithms

Paul R. Rosenbaum — Causal Inference

Scott Cunningham — Causal Inference

Mariusz Maziarz — The Philosophy of Causality in Economics: Causal Inferences and Policy Proposals

Babette A. Brumback, Babette A. — Fundamentals of Causal Inference with R

Momiao Xiong — Artificial Intelligence and Causal Inference

William R. Shadish, Thomas D. Cook, Donald Thomas Campbell — Experimental and Quasi-Experimental Designs for Generalized Causal Inference

Andrew Gelman, Xiao-Li Meng — Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics)

Stephen L. Morgan, Christopher Winship — Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research)

David A. Freedman; David Collier, Jasjeet S. Sekhon, Philip B. Stark (Editors) — Statistical Models and Causal Inference: A Dialogue with the Social Sciences

Judea Pearl — Causality: Models, Reasoning, and Inference, 1e

Guido W. Imbens, Donald B. Rubin — Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

Judea Pearl — Causality: Models, Reasoning and Inference

Paul R. Rosenbaum — Observation and Experiment: An Introduction to Causal Inference

NICK. HUNTINGTON-KLEIN — The Effect: An Introduction to Research Design and Causality

Samantha Kleinberg — Causality, Probability, and Time

Stephen Morgan, Christopher Winship — Counterfactuals and Causal Inference: Methods and Principles for Social Research

Judea Pearl — Causal Inference in Statistics

Silvia Bacci, Bruno Chiandotto — Introduction To Statistical Decision Theory: Utility Theory And Causal Analysis

Xiong, Momiao — Big data in omics and imaging. Integrated analysis and causal inference

John Losee — Theories of Causality: From Antiquity to the Present

Paul R. Rosenbaum (auth.) — Design of observational studies

Hector Geffner, Rita Dechter, Joseph Halpern — Probabilistic and Causal Inference

Bibhas Chakraborty, Erica E.M. Moodie (auth.) — Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine

Samantha Kleinberg — Why: A Guide to Finding and Using Causes

by Steven C. Coughlin — Causal inference and scientific paradigms in epidemiology

Rina Dechter, Hector Geffner, Joseph Y. Halpern — Heuristics, Probability, and Causality. Tribute to Judea Pearl

Stephen L. Morgan (auth.), Stephen L. Morgan (eds.) — Handbook of Causal Analysis for Social Research

朱迪亞 · 珀爾 (Judea Pearl), 達納 · 麥肯錫 (Dana Mackenzie) 著 ; 甘錫安譯 — 因果革命：人工智慧的大未來 = The Book of Why: The New Science of Cause and Effect

Ключові фрази