Causal Inference
Causal Inference - Related Videos
10:08
Categories and Inductive Inferences
0 Views •
2023
Source: Laboratories of Nicholaus Noles and Judith Danovitch—University of Louisville It might be possible for the human brain to keep track of each individual person, place, or thing encountered, but that would be a very inefficient use of time and cognitive resources. Instead, humans develop categories. Categories are mental representations of real things that can be used for a variety of purposes. For example, individuals can use the perceptual features of animals to place them into a given...
01:15
Theory of Attribution I: Correspondent Inference Theory
0 Views •
2025
Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
01:16
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
0 Views •
2024
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics. Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
11:10
Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
0 Views •
Cited by 20 •
2010
HIV tropism can be inferred from the V3 region of the viral envelope. V3 is PCR amplified in triplicate using nested RT-PCR, sequenced, and interpreted using bioinformatic software. Samples with with 1 or more sequence(s) with low g2P scores are classified as non-R5 virus.
10:44
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
0 Views •
Cited by 3 •
2021
The Inherent Dynamics Visualizer is an interactive visualization package that connects to a gene regulatory network inference tool for enhanced, streamlined generation of functional network models. The visualizer can be used to make more informed decisions for parameterizing the inference tool, thus increasing confidence in the resulting models.
FAQs
Counterfactual reasoning asks researchers to compare an observed outcome with the outcome that would have occurred under an alternative condition. Because only one condition is directly observed for a person or group, this comparison provides a framework for estimating what changed because of the relevant variable. It helps distinguish a produced effect from a simple association.
A confounding variable can influence both the factor being studied and the outcome, creating an apparent relationship that does not reflect the factor’s true effect. Addressing confounding is therefore essential when interpreting psychological findings. Causal diagrams help represent these relationships, while statistical adjustment and matching provide ways to reduce their distortion in observational research.
Randomized experiments use controlled assignment, which helps separate the effect of the studied condition from preexisting differences between groups. Observational studies do not assign conditions and must instead estimate effects through approaches such as statistical adjustment, matching, and causal diagrams. The distinction matters because the available design determines how researchers address confounding when evaluating psychological effects.
Causal diagrams organize proposed relationships among variables and make potential confounding pathways more explicit. In observational studies, this structure can guide decisions about which relationships require statistical attention or matching. Their value is not simply visual: they help researchers clarify assumptions about how variables may connect before estimating effects related to behavior, cognition, or mental health.
A study begins by specifying the variable whose effect is being examined and the outcome of interest, then considering the alternative condition required for counterfactual comparison. Researchers next choose a randomized or observational design, identify possible confounding variables, and apply controlled assignment or methods such as adjustment, matching, or causal diagrams. The resulting estimates must be interpreted within that design.
Observational methods are especially relevant when randomization is not feasible. In that setting, researchers can use statistical adjustment, matching, and causal diagrams to estimate effects while addressing confounding variables. These methods support investigation of psychological phenomena in situations where controlled assignment cannot be used, although the credibility of the conclusions depends on how effectively the study handles alternative explanations.
Causal inference helps clarify whether an intervention or other variable produces changes in psychological outcomes rather than merely occurring alongside them. In mental health, behavioral, and cognitive research, this distinction supports more informative evaluation of interventions and potential mechanisms. Findings can therefore contribute to evidence-based practice by connecting observed changes with a reasoned estimate of their causes.