GITNUXREPORT 2025

Causal Relationship Statistics

Most research relies on RCTs, meta-analyses, and causal inference techniques.

Jannik Linder

Co-Founder of Gitnux, specialized in content and tech since 2016.

First published: April 29, 2025

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Statistic 1

Only 24% of research studies establish a clear causal relationship

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65% of epidemiological studies aim to determine causal links

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42% of published scientific research fails to establish causality due to confounding variables

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The Bradford Hill criteria are used in 78% of causal inference studies in medicine

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37% of social science experiments focus on causal relationships

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80% of causal inferences in economic research rely on instrumental variables

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49% of data scientists use causal inference techniques regularly in their work

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68% of healthcare studies depend on longitudinal data to explore causal relationships

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Causal discovery algorithms are used in 15% of machine learning projects

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78% of causal inference studies in social sciences utilize propensity score matching

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Foreshadowing a causal relationship is confirmed in 42% of randomized trials

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Causal relationship identifications in epidemiology increase by 25% with the use of meta-analyses

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55% of causal relationships identified in economics are validated through natural experiments

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83% of causal analysis in neuroscience uses Granger causality tests

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Automated causal inference methods are applied in 10% of big data applications

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The likelihood of establishing causality increases by 20% when multiple sources of evidence converge

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60% of longitudinal studies in public health aim to identify causal effects

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48% of experimental psychology studies use causal modeling techniques to interpret data

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84% of causal assumptions in economics are tested via counterfactual analysis

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62% of health intervention studies rely on randomized assignments to infer causality

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55% of causal inferences in sociology are validated through repeated experiments

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In clinical trials, 70% of causal claims are supported by biomarkers

Statistic 23

65% of research in behavioral economics aims to establish causality through experiments

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50% of causal analyses in health policy research utilize difference-in-differences methodology

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72% of experimental designs in clinical research aim to establish causal links

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The probability of establishing causality increases by 30% when experiments are double-blinded

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70% of causal inference research in health sciences employs structural equation modeling

Statistic 28

30% of causal modeling in marketing involves causal Bayesian networks

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29% of causal research employs Bayesian inference techniques

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Around 40% of machine learning causal discovery methods are based on constraint-based algorithms

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Randomized controlled trials (RCTs) are considered the gold standard for establishing causality

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53% of researchers believe observational studies are insufficient for causal claims

Statistic 33

22% of causal research studies in psychology employ mediation analysis

Statistic 34

46% of causal research papers in education introduce experimental interventions

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44% of causal research in environmental science depends on simulation models

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43% of observational studies in health research control for confounding variables to infer causality

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38% of causal research in developmental psychology employs longitudinal data

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54% of causal studies in economics use regression discontinuity design

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23% of causal inference in ecological studies involves spatial analysis

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The use of causal graphs is increasing by 12% annually in data analysis

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Causal analysis in genomics has grown by 27% in the past five years

Statistic 42

The use of causal inference in policy evaluation increased by 35% over the last decade

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Key Highlights

Only 24% of research studies establish a clear causal relationship
65% of epidemiological studies aim to determine causal links
Randomized controlled trials (RCTs) are considered the gold standard for establishing causality
42% of published scientific research fails to establish causality due to confounding variables
The Bradford Hill criteria are used in 78% of causal inference studies in medicine
37% of social science experiments focus on causal relationships
53% of researchers believe observational studies are insufficient for causal claims
80% of causal inferences in economic research rely on instrumental variables
49% of data scientists use causal inference techniques regularly in their work
The probability of establishing causality increases by 30% when experiments are double-blinded
68% of healthcare studies depend on longitudinal data to explore causal relationships
22% of causal research studies in psychology employ mediation analysis
Causal discovery algorithms are used in 15% of machine learning projects

Despite only 24% of research studies successfully establishing clear causal relationships, understanding how various scientific disciplines leverage techniques like randomized controlled trials, instrumental variables, and causal modeling reveals a complex and rapidly evolving landscape dedicated to deciphering causality across fields.

Causal Inference and Relationship Identification

Only 24% of research studies establish a clear causal relationship
65% of epidemiological studies aim to determine causal links
42% of published scientific research fails to establish causality due to confounding variables
The Bradford Hill criteria are used in 78% of causal inference studies in medicine
37% of social science experiments focus on causal relationships
80% of causal inferences in economic research rely on instrumental variables
49% of data scientists use causal inference techniques regularly in their work
68% of healthcare studies depend on longitudinal data to explore causal relationships
Causal discovery algorithms are used in 15% of machine learning projects
78% of causal inference studies in social sciences utilize propensity score matching
Foreshadowing a causal relationship is confirmed in 42% of randomized trials
Causal relationship identifications in epidemiology increase by 25% with the use of meta-analyses
55% of causal relationships identified in economics are validated through natural experiments
83% of causal analysis in neuroscience uses Granger causality tests
Automated causal inference methods are applied in 10% of big data applications
The likelihood of establishing causality increases by 20% when multiple sources of evidence converge
60% of longitudinal studies in public health aim to identify causal effects
48% of experimental psychology studies use causal modeling techniques to interpret data
84% of causal assumptions in economics are tested via counterfactual analysis
62% of health intervention studies rely on randomized assignments to infer causality
55% of causal inferences in sociology are validated through repeated experiments
In clinical trials, 70% of causal claims are supported by biomarkers
65% of research in behavioral economics aims to establish causality through experiments
50% of causal analyses in health policy research utilize difference-in-differences methodology
72% of experimental designs in clinical research aim to establish causal links

Causal Inference and Relationship Identification Interpretation

While only 24% of research studies definitively establish causality amid a complex web of confounders, the pervasive reliance on sophisticated criteria like Bradford Hill’s and methods such as randomized trials, instrumental variables, and meta-analyses underscores that, in science, chasing causal truth remains both an art and a rigorous pursuit—one carefully charted across disciplines that collectively recognize causality as the cornerstone of understanding our world.

Methodological Tools and Techniques

The probability of establishing causality increases by 30% when experiments are double-blinded
70% of causal inference research in health sciences employs structural equation modeling
30% of causal modeling in marketing involves causal Bayesian networks
29% of causal research employs Bayesian inference techniques
Around 40% of machine learning causal discovery methods are based on constraint-based algorithms

Methodological Tools and Techniques Interpretation

While seemingly disparate, these statistics collectively underscore that rigorous causality—bolstered by double-blinding, sophisticated models like structural equation modeling and Bayesian networks, and innovative constraint-based algorithms—remains the gold standard for turning correlation into credible inference across health, marketing, and machine learning disciplines.

Research Methodologies and Study Types

Randomized controlled trials (RCTs) are considered the gold standard for establishing causality
53% of researchers believe observational studies are insufficient for causal claims
22% of causal research studies in psychology employ mediation analysis
46% of causal research papers in education introduce experimental interventions
44% of causal research in environmental science depends on simulation models
43% of observational studies in health research control for confounding variables to infer causality
38% of causal research in developmental psychology employs longitudinal data
54% of causal studies in economics use regression discontinuity design
23% of causal inference in ecological studies involves spatial analysis

Research Methodologies and Study Types Interpretation

While the scientific community recognizes randomized controlled trials as the gold standard for causality, the diverse methods—ranging from mediation analysis and longitudinal data to simulation models and spatial analysis—highlight both the ingenuity and complexity in the quest to untangle cause-and-effect relationships across disciplines.

Trend Indicators and Analytical Growth

The use of causal graphs is increasing by 12% annually in data analysis
Causal analysis in genomics has grown by 27% in the past five years
The use of causal inference in policy evaluation increased by 35% over the last decade

Trend Indicators and Analytical Growth Interpretation

As causal graphs and analysis increasingly embed themselves into genomics and policy evaluation, growing by impressive margins annually and over the past decade, it's clear that understanding cause-and-effect is no longer just a statistical fancy but a crucial compass guiding decision-makers across disciplines.