Categories → Causal Inference , Study Design
Observational studies involve use of existing, historical data from observing and measuring the real world, without manipulating any variables.
Observational data refers to data that has been collected by observing and measuring phenomena in the real world, rather than manipulating variables in a controlled laboratory setting as part of an interventional experiment such as a randomized controlled trial. Observational data is commonly used in social sciences, epidemiology, and other fields where it may be unethical or impractical to conduct experimental research.
Observational experimental design is a research methodology that involves observing individuals or groups and collecting data on their behavior, attitudes, or other characteristics. The data may have been collected for another incidental purpose, such as another business function. Observational data includes historical data that companies already possess. The process of selecting, filtering, cleaning and preparing data produces a Dataset.
In this type of design, the researcher does not manipulate any variables, but instead observes and measures them as they naturally occur. This approach can provide valuable insights into how individuals or groups behave and interact with one another in real-world settings. If the same individuals or entities are observed over time, the dataset might match the Panel Data format.
However, because the researcher does not have control over the variables being studied, observational data can sometimes lead to confounding variables or other biases that can make it difficult to draw causal conclusions. For example, in a study on the relationship between smoking and lung cancer, a confounding variable like air pollution could also be contributing to the development of lung cancer in individuals who smoke.
Despite these challenges, observational data can still yield causal insights when using causal inference methods. One such method is propensity score matching, which is a statistical technique that allows researchers to control for confounding variables by matching individuals with similar characteristics. For example, in the smoking and lung cancer study, researchers could use propensity score matching to match individuals who smoke, with individuals who do not smoke, but have similar levels of exposure to air pollution, age, sex, and other relevant factors. By controlling for these confounding variables, researchers can more accurately estimate the causal effect of smoking on lung cancer.
Another method is instrumental variable analysis, which uses natural experiments or other sources of variation in the data to estimate causal effects. For example, in a study on the impact of education on income, researchers could use an instrumental variable like proximity to a university to estimate the causal effect of education on income, while controlling for other factors like ability and motivation.
In conclusion, while observational data can present additional challenges for causal inference compared to randomized controlled trials, it can still provide valuable insights when using appropriate causal inference methods.