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Social Sciences185

Environment & Sustainability160

手法統計学1,836 人工知能・機械学習1,661 意思決定科学932 研究方法1,354 測定1,745 因果とエビデンス532 研究実践118

493 件の手法 · 因果とエビデンスクリア

フィルターに一致する実在の手法。

並べ替え人気順 A–Z Z–A 新着順

Health Protective Behavior Scale

The Health Protective Behavior Scale (HPBS) assesses self-reported engagement in preventive behaviors during infectious disease outbreaks, including hand hygiene, respiratory etiquette, isolation, and vaccination. Developed from literature review and behavioral theory by Bish and Michie, and refined through implementat

2件の出典2011

healthcare management

Health Technology Assessment

Health Technology Assessment (HTA) is a structured, multidisciplinary approach to evaluating the clinical, economic, and societal effects of healthcare technologies (devices, drugs, procedures, systems). HTA synthesizes evidence from clinical trials, observational studies, and economic analyses to support decision-make

3件の出典1980

health behavior

Health-Promoting Lifestyle Profile II

The Health-Promoting Lifestyle Profile II (HPLP-II) is a 52-item self-report instrument developed by Walker, Sechrist, and Pender in 1987 to assess and measure health-promoting behaviors across multiple life domains. Based on Pender's Health Promotion Model, the HPLP-II evaluates six dimensions of positive health behav

2件の出典1987

healthcare management

Healthcare Team Vitality Instrument

The Healthcare Team Vitality Instrument (HTVI) is a brief, 5-item survey designed to measure healthcare team cohesion, communication quality, and shared purpose—dimensions of team "vitality" that are associated with effective teamwork and patient safety. Developed by Metersky and colleagues and validated in intensive c

3件の出典2015

Healthcare Worker COVID-19 Burnout Scale

The Healthcare Worker COVID-19 Burnout Scale (HWCBS) measures occupational burnout specific to pandemic-era healthcare work, including emotional exhaustion, depersonalization, and reduced personal accomplishment under pandemic stress. Adapted from the Maslach Burnout Inventory (MBI) by Lan and colleagues for COVID-19 c

2件の出典2020

public health nutrition

HEI-2020

The HEI-2020 is a composite score measuring diet quality based on adherence to the 2020–2025 Dietary Guidelines for Americans. Developed by USDA and the National Cancer Institute, the HEI evaluates 13 dietary components: adequacy of fruit, vegetables, grains, protein foods, dairy; moderation of saturated fat, added sug

2件の出典2020

causal inference

Heterogeneous treatment effect Causal impact analysis

Heterogeneous treatment effect causal impact analysis extends the Bayesian structural time-series causal impact framework to estimate not just the average effect of an intervention but how that effect varies across subgroups or individual units. By combining counterfactual prediction with conditional average treatment

2件の出典2015

causal inference

Heterogeneous Treatment Effect Coarsened Exact Matching

Heterogeneous treatment effect coarsened exact matching (HTE-CEM) extends the coarsened exact matching framework to estimate how treatment effects vary across subgroups or individual characteristics. After CEM creates balanced strata by coarsening continuous covariates into bins and exactly matching units within each b

2件の出典2012

causal inference

Heterogeneous treatment effect Counterfactual impact evaluation

Heterogeneous Treatment Effect Counterfactual Impact Evaluation (HTE-CIE) extends standard counterfactual impact evaluation by estimating how the causal effect of a policy or intervention varies across subgroups defined by pre-treatment characteristics. Rather than reporting a single average treatment effect, it maps t

2件の出典2010

causal inference

Heterogeneous Treatment Effect Difference-in-Differences

HTE-DiD extends the classic Difference-in-Differences estimator to settings where treatment effects vary across units, time periods, or treatment cohorts. Developed formally by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it avoids the biases that arise when a conventional two-way fixed-effects regression

2件の出典2021

causal inference

Heterogeneous treatment effect Doubly robust estimation

Doubly robust estimation of heterogeneous treatment effects (HTE) estimates how the causal effect of a treatment varies across subgroups or individual covariate values. By combining an outcome model and a propensity score model, it retains consistency if either model is correctly specified, and supports flexible machin

2件の出典2018

causal inference

Heterogeneous Treatment Effect Entropy Balancing

Heterogeneous Treatment Effect Entropy Balancing combines entropy balancing — a preprocessing step that reweights control units to match the treatment group on covariate moments — with methods that estimate how the treatment effect varies across subgroups or individuals. It produces covariate-balanced weights without p

2件の出典2012

causal inference

Heterogeneous Treatment Effect Event Study Design

Heterogeneous Treatment Effect Event Study Design is a causal-inference framework that uses event study regression to estimate how treatment effects vary across groups, cohorts, or time relative to a treatment event. Unlike classical two-way fixed-effects event studies — which assume a homogeneous effect — this approac

2件の出典2021

causal inference

Heterogeneous Treatment Effect Fuzzy Regression Discontinuity

Heterogeneous Treatment Effect Fuzzy RDD extends the standard fuzzy regression discontinuity design — where treatment probability, not treatment status itself, jumps at a threshold — by examining whether the Local Average Treatment Effect (LATE) estimated at the threshold differs systematically across subgroups defined

2件の出典2001

causal inference

Heterogeneous treatment effect Instrumental variables

Heterogeneous treatment effect IV applies instrumental variables estimation while explicitly acknowledging and modelling that the treatment effect differs across units. Rather than recovering a single average effect, it focuses on the Local Average Treatment Effect (LATE) — the causal effect for compliers, the subpopul

2件の出典1994

causal inference

Heterogeneous Treatment Effect Interrupted Time Series

Heterogeneous Treatment Effect Interrupted Time Series extends the standard ITS design to detect whether an intervention's effect on a time series differs systematically across subgroups or in response to unit-level moderators. Where ordinary ITS yields a single level-change and slope-change estimate, HTE-ITS adds inte

2件の出典2000

causal inference

Heterogeneous Treatment Effect Inverse Probability Weighting

HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, a

2件の出典2003

causal inference

Heterogeneous Treatment Effect Marginal Structural Model

The Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations with inverse probability of treatment weights (IPTW) and interacting the treatm

2件の出典2000

causal inference

Heterogeneous Treatment Effect Matching Estimator

The Heterogeneous Treatment Effect (HTE) Matching Estimator extends standard matching to recover how treatment impacts differ across subgroups or covariate values. Rather than reporting a single average treatment effect, it pairs treated and control units on observed characteristics and then estimates the conditional a

2件の出典1997

causal inference

Heterogeneous treatment effect Panel event study

A heterogeneous treatment effect panel event study estimates how treatment impacts vary across units and over time in a panel setting, allowing each cohort of treated units to have its own dynamic response. Seminal contributions by Sun and Abraham (2021) and Callaway and Sant'Anna (2021) showed that standard two-way fi

2件の出典2021

causal inference

Heterogeneous treatment effect Placebo test

A placebo test for heterogeneous treatment effects is a falsification strategy used to validate whether estimated variation in treatment effects across subgroups or covariate values is genuine rather than an artifact of model specification, overfitting, or coincidental patterns. By applying the same estimation procedur

2件の出典2000

causal inference

Heterogeneous Treatment Effect Propensity Score Matching

Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing whi

2件の出典1983

causal inference

Heterogeneous Treatment Effect Regression Discontinuity Design

Heterogeneous Treatment Effect RDD extends the classic regression discontinuity framework to detect and estimate how the causal effect of crossing an assignment cutoff varies across subgroups or along covariates. Rather than reporting a single local average treatment effect at the threshold, HTE-RDD maps how treatment

2件の出典2015

causal inference

Heterogeneous Treatment Effect Sensitivity Analysis for Causality

Heterogeneous Treatment Effect Sensitivity Analysis examines how robust subgroup-specific causal estimates are to unobserved confounding. Rather than testing a single average treatment effect, it asks whether the estimated variation in treatment effects across units or subgroups could be explained away by hidden bias,

2件の出典2000

causal inference

Heterogeneous Treatment Effect Synthetic Control Method

The Heterogeneous Treatment Effect Synthetic Control Method (HTE-SCM) extends the classical synthetic control framework by allowing the causal effect of an intervention to vary across time periods, subgroups, or outcome dimensions rather than collapsing it to a single average estimate. It combines the counterfactual do

2件の出典2010

causal inference

Heterogeneous Treatment Effects

Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (

2件の出典2018

public health nutrition

HFEQ

The HFEQ is a parent-report questionnaire measuring the household food environment—the availability of healthy and unhealthy foods, parent feeding practices, and family mealtime characteristics. Developed by Boles, Fulkerson, and colleagues, the HFEQ captures multiple dimensions of the home environment that influence c

2件の出典2014

public health nutrition

HFIAS

The HFIAS is a 9-item survey designed to measure the frequency and severity of food insecurity at the household level in resource-limited settings. Developed by the FANTA Project in 2007, it assesses four domains of food access: anxiety, dietary diversity, food consumption frequency, and household member deprivation. I

1件の出典2007

public health nutrition

HFSM

The HFSM is the official U.S. government measure of household food security, used in the Current Population Survey and National Health and Nutrition Examination Survey since 1995. The 18-item full form and 6-item short form assess the frequency and severity of food insecurity within a household based on direct reports

2件の出典1999

healthcare management

Hospital Bed Occupancy Model

Hospital bed occupancy models forecast the number of occupied beds at future times by analyzing admission patterns, length of stay distributions, and discharge dynamics. These models support tactical decisions about staffing, supply chain management, and strategic decisions about capacity expansion.

3件の出典2000

healthcare management

Hospital Readmission Prediction Model

Hospital readmission prediction models use statistical and machine learning techniques to identify patients at high risk of returning to the hospital shortly after discharge. These models guide targeted discharge planning and follow-up to improve outcomes and reduce costs.

3件の出典1998

healthcare management

Hospital Survey on Patient Safety Culture

The Hospital Survey on Patient Safety Culture (HSOPS) is a 42-item standardized instrument developed by the Agency for Healthcare Research and Quality (AHRQ) to measure patient safety culture in hospital settings. First released in 2004 and revised in 2018, the HSOPS assesses 12 composite dimensions of safety culture a

3件の出典2004

Hunt and Hess Scale

The Hunt and Hess Scale is the most widely used clinical grading system for assessing severity and prognosis in subarachnoid hemorrhage (SAH) caused by ruptured intracranial aneurysm. Developed by neurosurgeons William Hunt and Robert Hess in 1968, the five-point ordinal scale measures level of consciousness and presen

1件の出典1968

health outcomes

IBDQ

The IBDQ is a disease-specific quality of life measure for inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis. Developed by Elena Irvine and colleagues in 1994, this 32-item questionnaire measures how IBD affects bowel function, systemic symptoms, emotional well-being, and social functio

3件の出典1994

implementation science

ICS

The Implementation Climate Scale (ICS) is a brief organizational assessment tool that measures the extent to which an organization's work climate, policies, and systems are aligned with and supportive of evidence-based practice (EBP) implementation. Developed by Ehrhart, Aarons, and Farahnak in 2014, the ICS measures f

1件の出典2014

health behavior

Illness Perception Questionnaire Revised

The Illness Perception Questionnaire—Revised (IPQ-R) is a 70-item measure (brief version: 38 items) developed by Moss-Morris and colleagues (2002) to assess how individuals perceive and cognitively represent their illness. Based on Leventhal's Common Sense Model of illness representation, the IPQ-R measures nine dimens

1件の出典2002

implementation science

ILS

The Implementation Leadership Scale (ILS) is a 12-item self-report measure that assesses unit-level leadership behaviors critical to successful implementation of evidence-based practices and innovations. Developed by Aarons, Ehrhart, and Farahnak in 2014, the ILS measures four dimensions of implementation leadership: p

2件の出典2014

implementation science

Implementation Outcome Taxonomy

The Implementation Outcome Taxonomy is a framework defining eight measurable dimensions for assessing implementation success: Acceptability, Adoption, Appropriateness, Feasibility, Fidelity, Implementation Cost, Penetration, and Sustainability. Developed by Proctor et al. (2011), it provides a standardized vocabulary a

3件の出典2011

evidence synthesis

Individual Patient Data Meta-Analysis

Individual patient data meta-analysis (IPD-MA) is a systematic synthesis method where researchers obtain and analyze raw data at the patient level from multiple randomized controlled trials, rather than relying on published summary statistics (aggregate data). Pioneered by the Cochrane Collaboration and formalized by S

3件の出典1990

causal inference

Instrumental Variables in Education Research

Instrumental variables (IV) estimation is a quasi-experimental strategy for isolating the causal effect of schooling or educational interventions when assignment to treatment is confounded by unobserved factors. Pioneered in education economics by Angrist and Krueger's use of quarter-of-birth as an instrument for compu

2件の出典1991

health economics

Instrumental Variables in Health Research

Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the

3件の出典1990

health services

International Prostate Symptom Score

The International Prostate Symptom Score (IPSS) is a validated seven-item self-report instrument adopted by the World Health Organization and American Urological Association to measure the severity of lower urinary tract symptoms (LUTS) in men with suspected benign prostatic hyperplasia (BPH). The IPSS comprises items

3件の出典1992

causal inference

Interrupted Time Series

Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial b

2件の出典2002

causal inference

Interrupted Time Series in Education Research

Interrupted time series (ITS) analysis is a quasi-experimental design that estimates the causal effect of an education policy or intervention by examining whether an outcome trend changes abruptly at the point of implementation. Applied to education, it is used to evaluate reforms, curriculum changes, testing policies,

2件の出典1979

causal inference

Inverse Probability Weighting

Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is

2件の出典2000

causal inference

Inverse Probability Weighting in Education Research

Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they received the treatment — thereby creating a pseudo-population in which programme part

2件の出典1983

health education

IPCS

The IPCS is a self-report questionnaire measuring healthcare professionals' and students' attitudes, beliefs, and behaviors regarding interprofessional collaboration and teamwork. Developed through research by Hind and colleagues in 2003 and refined in subsequent interprofessional education studies, the IPCS evaluates

2件の出典2003

public health nutrition

IYCF Practices

The IYCF assessment is a set of core indicators developed by WHO and UNICEF to measure the prevalence of key feeding practices in children aged 0–23 months. The indicators track six essential markers: exclusive breastfeeding in infants under 6 months, continued breastfeeding in the second year, timely introduction of c

2件の出典2021

health outcomes

KDQOL

The KDQOL is the most widely used quality of life measure for chronic kidney disease (CKD) patients, particularly those on dialysis. Developed by Ron Hays and colleagues in 1994, this multidimensional questionnaire (full version 134 items; short-form KDQOL-SF 36 items) measures kidney disease-specific impacts on physic

3件の出典1994

implementation science

Knowledge Translation

Knowledge Translation (KT) is the systematic synthesis, dissemination, exchange, and application of research findings to improve health outcomes and healthcare practice. First formalized by the Canadian Institutes of Health Research in 2004, KT recognizes that evidence generation alone does not automatically change cli

3件の出典2004

implementation science

KTA

The Knowledge-to-Action (KTA) Framework is a conceptual model and process guide for translating evidence into practice, developed by Ian Graham and colleagues at the Ottawa Hospital Research Institute (2004–2006). The KTA framework addresses a central challenge in implementation science: research evidence alone does no

2件の出典2004

healthcare management

Lean Healthcare

Lean is a management philosophy that emerged from the Toyota Production System, focused on maximizing patient value while minimizing waste. Applied to healthcare, Lean uses systematic methods to identify and eliminate non-value-added activities, reduce wait times, and improve the quality of patient care.

3件の出典1988

evidence synthesis

Living Systematic Review

A living systematic review (LSR) is a dynamic, continuously updated evidence synthesis that monitors emerging literature and incorporates new studies as they become available, rather than being a static document published once. Formalized by Elliott et al. (2017) and adopted by the Cochrane Collaboration, living system

3件の出典2017

causal inference

Local Average Treatment Effect

The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compl

2件の出典1994

Lockdown Wellbeing Scale

The Lockdown Wellbeing Scale (LWS) measures the specific psychological and social impacts of mobility restrictions and lockdown policies on individual well-being. Developed by Giuntella and colleagues from economic and social data on pandemic restrictions, it captures dimensions of isolation, social disconnection, rout

2件の出典2021

causal inference

Machine learning-augmented causal impact analysis

Machine learning-augmented causal impact analysis combines quasi-experimental counterfactual reasoning with flexible ML prediction models to estimate the causal effect of an intervention on a time series outcome. Building on Brodersen et al.'s Bayesian structural time series (BSTS) framework and extended by double/debi

2件の出典2015

causal inference

Machine Learning-Augmented Coarsened Exact Matching

Machine Learning-Augmented Coarsened Exact Matching extends Coarsened Exact Matching (Iacus, King & Porro, 2012) by using supervised machine learning to automate and optimise the coarsening step — the discretisation of continuous covariates into bins — rather than relying on researcher-specified cutpoints. This reduces

2件の出典2012

causal inference

Machine Learning-Augmented Counterfactual Impact Evaluation

Machine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather than imposing parametric functional forms for confounders, ML learners — such as lasso, random forests, or neural nets — estimate nuisance func

2件の出典2016

causal inference

Machine learning-augmented difference-in-differences

Machine learning-augmented DiD combines the classic difference-in-differences identification strategy with flexible ML estimators for nuisance functions — the propensity score and the outcome regression — to obtain valid causal estimates even when treatment selection and outcome dynamics are complex, high-dimensional,

2件の出典2018

causal inference

Machine learning-augmented doubly robust estimation

Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component i

2件の出典2018

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