ScholarGate
اكتشف
المكتبةمكتبتيالمنضدةفحص مسبقReview Studioالمساعد
مساحة العمل
قارن
أنشئ رفّ كتبك

احفظ الطرق، ونظّم المجموعات، وانقلها إلى منضدتك.

إنشاء حساب
المكتبة / تصفح
تسجيل الدخول
المكتبة

استكشف العلم حسب المنهج والمجال والأدلة.

فهرس واحد لمناهج البحث — تعرّف على طريقة عمل كل منهج، ومتى يُستخدم، وما الذي لا يستطيع فعله.

6,495 مناهج11 مجالات7 عائلات مناهج40 لغات
أطلس العلمارسم خريطة بنية العلم قبل أن تستخدمه.المجالات · المناهج · مسارات الأدلةاستكشف الخريطة
المجالHealth & Medicine716Psychology570Business & Finance410Engineering330Life Sciences263Education261Research Practice248
ScholarGate

مكتبة مرجعية يتصدّرها المحتوى لطرق البحث — ما كل طريقة، وكيف تعمل، ومن أين جاءت.

بيانات مفتوحة (CC-BY)

اكتشف

  • المكتبة
  • ابحث في الطرق…
  • تصفّح حسب المجال
  • المجالات
  • الرحلة
  • قارن
  • أي طريقة؟

مرجع

  • المواضيع
  • الأطلس
  • المسرد
  • المنهجية
  • الفلسفة

مساحة العمل

  • مكتبتي
  • المنضدة
  • محادثة

الشركة

  • حول
  • الأسعار
  • اتصل بنا
  • اقترح طريقة

المداخل مجمَّعة من مصادر منشورة لأغراض مرجعية. ويبقى التحقق من دقة أي معلومة ومدى ملاءمتها لاستخدامك الخاص مسؤوليتك وحدك.

© 2026 ScholarGate · مكتبة مرجعية لطرق البحث
  • الخصوصية
  • ملفات تعريف الارتباط
  • الشروط
  • حذف الحساب
Natural Sciences236
Social Sciences185
Environment & Sustainability160
Law30
المنهجالإحصاء1,836الذكاء الاصطناعي وتعلم الآلة1,661علوم القرار932مناهج البحث1,354القياس1,745السببية والأدلة532الممارسة البحثية118
493 مناهج · السببية والأدلةمسح
مناهج حقيقية مطابقة لعامل التصفية لديك.
ترتيبالشيوعأ–يي–أالأحدث
causal inference

Machine Learning-Augmented Entropy Balancing

Machine learning-augmented entropy balancing (ML-EB) combines Hainmueller's entropy balancing reweighting scheme with a machine-learning outcome model to produce a doubly-robust causal estimator. By jointly optimising covariate balance weights and a flexible predicted-outcome adjustment, ML-EB delivers consistent treat

2 مصادر2012
causal inference

Machine learning-augmented event study design

Machine learning-augmented event study design combines the standard event study framework — which traces outcome dynamics around a treatment date — with ML-based methods such as double/debiased machine learning (DML) or regularized regression to handle high-dimensional covariates, improve confounder control, and produc

2 مصادر2010
causal inference

Machine Learning-Augmented Fuzzy Regression Discontinuity

ML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. Where standard fuzzy RDD uses IV-style estimation at a threshold with imperfect compliance, the ML-augmented variant leverages nonparametric lea

2 مصادر2001
causal inference

Machine learning-augmented instrumental variables

Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instruments and model nuisance functions, thereby improving first-stage fit and enabl

2 مصادر2012
causal inference

Machine Learning-Augmented Interrupted Time Series

Machine Learning-Augmented Interrupted Time Series (ML-ITS) estimates the causal effect of a discrete intervention by training a machine learning model on pre-intervention time series data, projecting a counterfactual trajectory into the post-intervention period, and measuring the gap between observed and predicted out

2 مصادر2014
causal inference

Machine Learning-Augmented Inverse Probability Weighting

Machine learning-augmented inverse probability weighting replaces parametric logistic regression with flexible ML algorithms to estimate treatment propensity scores, then reweights the sample to balance treated and control units. By leveraging data-adaptive learners such as lasso, random forests, or gradient boosting,

2 مصادر2003
causal inference

Machine Learning-Augmented Marginal Structural Model

The machine learning-augmented marginal structural model combines the causal rigour of Robins et al.'s MSM framework with flexible, data-adaptive ML algorithms for estimating propensity scores and outcome models. By replacing parametric nuisance models with ensemble learners or neural networks, ML-MSMs recover valid ca

2 مصادر2000
causal inference

Machine Learning-Augmented Matching Estimator

The machine learning-augmented matching estimator combines classical nearest-neighbor or propensity-score matching with ML algorithms — such as lasso, random forests, or gradient boosting — to select covariates, estimate propensity scores, and correct for residual bias. The result is a matching-based causal estimator t

2 مصادر2006
causal inference

Machine Learning-Augmented Placebo Test

The machine learning-augmented placebo test is a causal-inference validation technique that uses flexible ML estimators — such as causal forests, LASSO, or double/debiased ML — to conduct falsification checks on an identification strategy. By replacing real treatment assignments with placebo (fake) assignments and veri

2 مصادر2010
causal inference

Machine Learning-Augmented Propensity Score Matching

Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The re

2 مصادر2004
causal inference

Machine learning-augmented propensity score weighting

Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability weights to balance treated and control groups. This reduces model-misspecificati

2 مصادر2010
causal inference

Machine learning-augmented regression discontinuity design

Machine learning-augmented regression discontinuity design (ML-RDD) combines the sharp identification logic of classical RDD — exploiting a known assignment cutoff in a running variable — with flexible, data-adaptive ML methods for bandwidth selection, conditional mean estimation, and covariate adjustment. The goal is

2 مصادر2019
causal inference

Machine Learning-Augmented Sensitivity Analysis for Causality

Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity

2 مصادر2018
causal inference

Machine Learning-Augmented Synthetic Control Method

The machine learning-augmented synthetic control method extends the classical synthetic control estimator by using penalized regression or other ML algorithms — such as lasso, ridge, or random forests — to construct the donor weights and to model pre-treatment outcome trajectories. The augmentation corrects for residua

2 مصادر2021
causal inference

Marginal structural model in education research

A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2000) in epidemiology and brought into education by Hong and Raudenbush (2006), M

2 مصادر2000
health economics

Markov Model in Health Economics

A Markov model is a decision-analytic tool that simulates disease progression through defined health states over time, calculating cumulative costs and quality-adjusted life years (QALYs) to enable cost-effectiveness analysis. Developed by Beck and Pauker in 1983, Markov models are now the standard framework for projec

3 مصادر1983
epidemiology

Matched case report

A matched case report is a structured clinical case write-up in which the index patient is compared against one or more systematically selected matched comparators — typically patients with similar demographics, comorbidities, or clinical settings who did not experience the same unusual outcome. The matched comparator

2 مصادر1990
epidemiology

Matched case-control study

A matched case-control study is an observational epidemiological design in which each case (a person with the disease or outcome of interest) is paired with one or more controls (persons without the outcome) who share one or more characteristics — such as age, sex, or clinical setting — to control confounding. Exposure

2 مصادر1950
epidemiology

Matched Case-Crossover Design

The matched case-crossover design is a self-controlled observational study in which each case serves as its own control. A short hazard window immediately before the acute event is compared with one or more matched control windows — selected to have the same day of week, season, or other time-varying covariate — making

2 مصادر1991
epidemiology

Matched Cohort Study

A matched cohort study is an observational design in which each exposed participant is paired with one or more unexposed counterparts who share key characteristics — such as age, sex, or comorbidity status — before both groups are followed forward in time to compare incident outcomes. Matching controls for measured con

2 مصادر1983
epidemiology

Matched Cross-Sectional Epidemiological Study

A matched cross-sectional epidemiological study is an observational design that measures exposure and outcome simultaneously in a population sample while applying matching to control for one or more confounding variables. By pairing or grouping participants on key characteristics such as age, sex, or socioeconomic stat

2 مصادر1970
epidemiology

Matched Diagnostic Accuracy Study

A matched diagnostic accuracy study evaluates how well an index test correctly identifies a target condition in study participants who have been matched on key characteristics — such as age, sex, or disease severity — to control for confounding. By pairing diseased and non-diseased subjects on relevant factors before a

2 مصادر1990
epidemiology

Matched dose-response analysis

Matched dose-response analysis evaluates whether increasing levels of exposure are associated with proportionally increasing (or decreasing) risk of an outcome, within a study where cases and controls — or exposed and unexposed individuals — have been deliberately matched on key confounders such as age, sex, or study s

2 مصادر1970
epidemiology

Matched ecological study

A matched ecological study is an observational epidemiological design in which aggregate units — such as geographic areas, communities, or time periods — are systematically paired or matched on key characteristics before comparing exposure and outcome rates. Matching at the group level controls for area-level confounde

2 مصادر1970
epidemiology

Matched nested case-control

A matched nested case-control study is an efficient observational design embedded within a defined cohort. When a participant develops the outcome of interest (a case), a small number of controls are sampled from those still at risk at that moment and matched to the case on key variables such as age, sex, or calendar t

2 مصادر1970
epidemiology

Matched Phase II clinical trial

A matched Phase II clinical trial is a single-arm or small-controlled early-efficacy study in which treated patients are paired with matched controls — drawn from historical databases, registries, or concurrent external cohorts — on key prognostic variables such as age, disease stage, and performance status. This desig

2 مصادر1960
epidemiology

Matched Phase III Clinical Trial

A matched Phase III clinical trial is a confirmatory, late-stage controlled study in which each participant assigned to the experimental treatment is paired with one or more controls who share key prognostic characteristics — such as age, disease stage, or comorbidities — before treatment allocation. By ensuring baseli

2 مصادر1950
epidemiology

Matched Phase IV Study

A Matched Phase IV study is a post-marketing observational design in which patients who received an approved drug (or intervention) are matched to comparable non-exposed patients — or patients on an alternative therapy — to evaluate real-world safety, effectiveness, or long-term outcomes. Conducted after regulatory app

2 مصادر1980
epidemiology

Matched Randomized Clinical Trial

A matched randomized clinical trial pairs participants (or clusters) on key baseline characteristics before randomization, then allocates one member of each pair to treatment and the other to control. This design combines the causal validity of randomization with the covariate balance of matching, increasing statistica

2 مصادر2000
epidemiology

Matched Screening Test Evaluation

Matched screening test evaluation assesses the sensitivity, specificity, and predictive values of a screening or diagnostic test using a matched design, in which disease-positive cases are paired with one or more disease-free controls selected to share key characteristics such as age, sex, or clinical setting. Matching

2 مصادر1980
causal inference

Matching Estimator

The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observat

2 مصادر1973
causal inference

Matching Methods

Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and geneti

2 مصادر2012
public health nutrition

MDQI

The Maternal Diet Quality Index (MDQI) is a composite measure of maternal nutrition that evaluates diet quality during pregnancy and postpartum using a scored framework. Adapted from general population dietary quality indices, the MDQI emphasizes nutrients critical for fetal development and maternal health: folate, iro

2 مصادر2010
causal inference

Mendelian Randomization

Mendelian randomization is a method for estimating causal effects of exposures on outcomes using genetic variants as instrumental variables. Introduced by George Davey Smith in the 1990s, it exploits Mendel's law of segregation to remove confounding bias. It has become a cornerstone technique in epidemiological causal

3 مصادر1997
academic writing

Meta-Analysis

Meta-analysis is the statistical pooling of quantitative findings from multiple independent studies to produce a combined effect estimate. By aggregating data across studies, meta-analysis increases statistical power, reduces random error, and provides a precise summary of an intervention's effectiveness or an associat

3 مصادر1976
epidemiology

Meta-analytic case report

A meta-analytic case report is a secondary research methodology that systematically identifies, appraises, and quantitatively or qualitatively pools data from multiple published individual case reports on the same clinical phenomenon. It is used most often when randomized trials or cohort data are unavailable — particu

2 مصادر2000
epidemiology

Meta-analytic Case Series

A meta-analytic case series is an evidence-synthesis design that systematically identifies, appraises, and statistically pools outcome data from multiple single-arm case series on a defined clinical condition or intervention. It occupies a middle tier of evidence — above individual case reports and unsystematic series,

2 مصادر2000
epidemiology

Meta-analytic case-control study

A meta-analytic case-control study systematically identifies, critically appraises, and quantitatively synthesizes data from multiple independent case-control studies examining the same exposure-disease relationship. By pooling odds ratios across studies, it yields a more precise and generalizable estimate of associati

2 مصادر1980
epidemiology

Meta-analytic case-crossover design

The meta-analytic case-crossover design combines the within-person control structure of the case-crossover study with formal meta-analytic pooling across multiple studies. Each contributing study uses cases as their own controls by comparing exposure windows immediately preceding an acute event to matched reference win

2 مصادر1991
epidemiology

Meta-analytic Cohort Study

A meta-analytic cohort study systematically identifies, appraises, and statistically pools the findings of two or more independent cohort studies addressing the same exposure-outcome relationship. By combining large prospective datasets, it provides more precise risk estimates than any single cohort alone, makes dose-r

2 مصادر1980
epidemiology

Meta-analytic cross-sectional epidemiological study

A meta-analytic cross-sectional epidemiological study systematically identifies and statistically pools prevalence or proportion estimates from multiple independent cross-sectional surveys. By combining data across studies — often using variance-stabilising transformations and random-effects models — it produces a more

2 مصادر2000
epidemiology

Meta-analytic Diagnostic Accuracy Study

A meta-analytic diagnostic accuracy study systematically identifies and pools sensitivity and specificity data from multiple primary diagnostic test accuracy studies. Using the bivariate or hierarchical summary ROC (HSROC) model, it produces a joint summary of a test's ability to correctly classify diseased and non-dis

2 مصادر1993
epidemiology

Meta-analytic dose-response analysis

Meta-analytic dose-response analysis pools summary statistics from multiple epidemiological studies to characterize how disease risk changes across ordered levels of an exposure. Rather than comparing a single high-exposure group against a reference, it reconstructs a continuous or categorical exposure-risk curve acros

2 مصادر1992
epidemiology

Meta-analytic Ecological Study

A meta-analytic ecological study synthesises data from multiple populations or geographic units — rather than from individual patients — to estimate associations between exposures and health outcomes. By pooling aggregate-level statistics across studies or regions, it extends the reach of ecological reasoning to a wide

2 مصادر1990
epidemiology

Meta-analytic Nested Case-Control

Meta-analytic nested case-control analysis combines the efficiency advantages of the nested case-control design — in which cases and matched controls are sampled from a defined cohort — with the statistical power and generalisability gained by pooling estimates from multiple such studies. This approach is especially va

2 مصادر1980
epidemiology

Meta-analytic Phase II clinical trial

A meta-analytic Phase II clinical trial integrates individual or aggregate data from multiple single-arm or small Phase II studies into a unified meta-analytic framework. Rather than relying on a single underpowered trial to screen for activity, this design pools evidence across comparable cohorts to obtain a more reli

2 مصادر2000
epidemiology

Meta-analytic Phase III Clinical Trial

A meta-analytic Phase III clinical trial is a systematic, quantitative synthesis of multiple Phase III randomized controlled trials (RCTs) examining the same intervention. By pooling confirmatory trial data under a pre-registered protocol, the approach yields more precise effect estimates, resolves conflicting findings

2 مصادر1976
epidemiology

Meta-analytic Phase IV Study

A meta-analytic Phase IV study pools and quantitatively synthesises data from multiple Phase IV (post-marketing) sources — including observational cohorts, registries, spontaneous adverse-event databases, and post-approval randomised trials — to produce a single, more precise estimate of a drug or device's real-world e

2 مصادر1990
epidemiology

Meta-analytic Randomized Clinical Trial

A meta-analytic randomized clinical trial is a formal evidence-synthesis method that identifies, appraises, and statistically combines the results of multiple randomized clinical trials addressing the same clinical question. By pooling trial-level data, it produces a single, more precise estimate of treatment effect an

2 مصادر1976
epidemiology

Meta-analytic Screening Test Evaluation

Meta-analytic screening test evaluation is a quantitative evidence-synthesis approach that pools sensitivity, specificity, and related accuracy indices across multiple primary studies of the same screening or diagnostic test. It produces summary estimates of a test's ability to correctly identify disease-positive and d

2 مصادر2000
epidemiology

Meta-analytic survival analysis

Meta-analytic survival analysis is a quantitative synthesis method that pools hazard ratios and related time-to-event statistics from multiple independent studies to produce a single, more precise estimate of a treatment or exposure effect on survival outcomes such as overall survival, disease-free survival, or time to

2 مصادر1990
meta analysis

Meta-Regression

Meta-regression is a statistical technique that extends conventional meta-analysis by regressing study-level effect sizes on one or more study characteristics (moderators) to explain between-study heterogeneity. Formalized by Thompson and Higgins in 2002, it uses weighted least squares — weighting each study by the inv

1 مصدر2002
scientometrics

meta-regression-based meta-analysis

Meta-regression-based meta-analysis extends standard meta-analysis by fitting a weighted regression model in which study-level characteristics (moderators) predict observed effect sizes. Rather than simply pooling effects, this approach asks why effects vary across studies — linking heterogeneity in outcomes to differe

2 مصادر1993
health informatics

Mobile Health Engagement Scale

The Mobile Health Engagement Scale measures the extent to which individuals engage with mobile health applications and digital behaviour change interventions. Developed through systematic review and meta-analysis by Perski and colleagues (2017), it captures both behavioural and psychological dimensions of engagement—fr

1 مصدر2017
causal inference

Moderation Analysis

Moderation analysis tests whether the effect of a predictor X on an outcome Y changes with the level of a third variable W, the moderator. It is estimated within a regression framework through an interaction term X×W, popularised by Aiken & West (1991) and Hayes's PROCESS macro (2018).

2 مصادر2018
causal inference

Multi-period Coarsened Exact Matching

Multi-period Coarsened Exact Matching (multi-period CEM) extends the CEM framework of Iacus, King, and Porro to longitudinal data with multiple pre- and post-treatment periods. It bins continuous covariates into coarsened categories, matches treated and control units that fall into the same cells across all relevant ti

2 مصادر2012
causal inference

Multi-period Counterfactual Impact Evaluation

Multi-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period evaluations, it tracks treatment effects as they evolve over time, capturing dynam

2 مصادر2000
causal inference

Multi-period Difference-in-differences

Multi-period Difference-in-Differences extends the classic two-period DiD framework to settings where units adopt treatment at different points in time. Formalised by Callaway and Sant'Anna (2021) and Goodman-Bacon (2021), it decomposes the overall treatment effect into group-time average treatment effects and addresse

2 مصادر2021
causal inference

Multi-period Doubly Robust Estimation

Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least on

2 مصادر1994
causal inference

Multi-period Event Study Design

The multi-period event study design estimates causal treatment effects at each point in time relative to the treatment onset, using panel data with multiple pre- and post-treatment periods. By plotting the full path of treatment coefficients rather than a single average, it reveals how effects build up, fade, or remain

2 مصادر1993
← 34 / 95 →