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ST4020 Causal Inference Assignment Sample NUI Galway Ireland

ST4020 Causal Inference course covers the study of identification and estimation of causal effects. Causal inference is the process of concluding cause-and-effect relationships from observational data. The course begins with an introduction to the potential outcomes framework, which is the standard approach to causal inference.

The focus is on the development of statistical methods for causal inference, with an emphasis on those that can be implemented using standard regression software. The course will cover the identification of causal effects, potential outcomes, selection models, instrumental variables, regression discontinuity designs, difference-in-differences, and synthetic control methods. The course will also cover the use of machine learning methods for causal inference, including propensity score methods and causal forest.

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In this section, we are describing some assigned activities. These are:

Assignment Activity 1: Describe the potential outcomes framework for causal inference.

The potential outcomes framework is the standard approach to causal inference. It consists of two parts: identification and estimation.

  • Identification is the process of determining which variables are likely to be associated with the outcome of interest.
  • Estimation is the process of estimating the size and direction of the causal effect.

The potential outcomes framework has been used extensively in the social sciences and has recently been adopted by the field of machine learning. Propensity score methods and causal forest are two examples of machine learning methods that use the potential outcomes framework for causal inference.

The potential outcomes framework is a powerful tool for causal inference, but it has some limitations.

  • First, it requires that the data be complete and correctly coded.
  • Second, it assumes that the data are generated by a process that is linear and additive. This assumption may not be realistic in many real-world situations.
  • Finally, the potential outcomes framework does not account for confounding factors that may be present in the data.

Despite these limitations, the potential outcomes framework is the best tool we have for causal inference. When used correctly, it can provide insights that would be otherwise impossible to obtain.

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Assignment Activity 2: Interpret directed acyclic graphs and derive minimal sets of variables for confounder adjustment.

A directed acyclic graph (DAG) is a graphical representation of a causal relationship between variables. A DAG can be used to identify the minimal set of variables that must be included in a confounder adjustment.

To identify the minimal set of variables, start with the outcome variable and work backward to the exposure variable.

For each variable, ask whether there is a direct path from that variable to the outcome.

  • If there is a direct path, then that variable must be included in the confounder adjustment.
  • If there is no direct path, then that variable does not need to be included.

The minimal set of variables is the smallest set of variables that includes all the variables that must be included in the confounder adjustment. Including any additional variables would not improve the accuracy of the confounder adjustment.

Assignment Activity 3: Estimate average treatment effects and standard errors for a binary treatment using IP weighting, outcome regression models, and doubly robust methods.

The average treatment effect (ATE) is the difference in outcomes between the treated and untreated groups. The ATE can be estimated using three different methods: IP weighting, outcome regression models, and doubly robust methods.

  • IP weighting is a method of estimating the ATE that uses information about the correlation between the treatment and the outcome.
  • Outcome regression models are a method of estimating the ATE that uses information about the relationship between the outcome and the confounders.
  • Doubly robust methods are a method of estimating the ATE that uses information from both IP weighting and outcome regression models.

ATE estimation is a complex topic, and there is no definitive answer about which method is best. Each method has advantages and disadvantages, and the best method to use depends on the specific situation.

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Assignment Activity 4: Implement flexible nonparametric models from Machine Learning for effect estimation.

There are a variety of machine learning models that can be used for effect estimation. Flexible nonparametric models are one type of model that can be used. These models are flexible in the sense that they can accommodate a variety of data structures and types of observations. They are also nonparametric in the sense that they do not require the data to conform to any specific distributional assumptions.

Flexible nonparametric models can be useful for estimating the effect of a treatment or exposure on an outcome of interest. For example, these models could be used to estimate the effect of a new medication on blood pressure or the effect of exposure to a new environmental pollutant on respiratory health.

There are many different types of flexible nonparametric models, and the best model to use in any given situation depends on the specific data and circumstances. Some examples of flexible nonparametric models include decision trees, random forests, and neural networks.

Assignment Activity 5: Outline the principles of estimating direct and indirect effects in mediation analyses for mechanisms of treatment effects.

Mediation analysis is a statistical technique that can be used to examine the mechanisms of treatment effects. Mediation analysis can be used to estimate the direct and indirect effects of a treatment on an outcome of interest.

The direct effect is the portion of the treatment effect that is due to the direct action of the treatment on the outcome. The indirect effect is the portion of the treatment effect that is due to the indirect action of the treatment on the outcome through its effects on mediator variables.

Mediation analysis can be used to estimate the direct and indirect effects of a variety of treatments, including medical interventions, educational interventions, and social welfare policies.

There are many different methods for conducting mediation analysis, and the best method to use in any given situation depends on the specific data and circumstances.

Some common methods for conducting mediation analysis include Baron and Kenny’s method, Sobel’s test, and the bootstrap method.

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Assignment Activity 6: Describe assumptions and effect estimation using instrumental variables, with applications in randomized controlled trials with nonadherence, and genetic epidemiology.

Instrumental variables (IV) are a type of statistical technique that can be used to estimate the effect of a treatment or exposure on an outcome of interest. IVs are used in situations where it is not possible to randomize the treatment or exposure, and therefore traditional methods for estimating the effect are not available.

IVs are used in a variety of settings, including randomized controlled trials with nonadherence and genetic epidemiology. In randomized controlled trials with nonadherence, IVs can be used to estimate the effect of the treatment on the outcome when there is some nonadherence to the treatment. In genetic epidemiology, IVs can be used to estimate the effect of a gene on an outcome when the gene is not randomly assigned.

Assignment Activity 7: Define explicit treatments for a “target trial” to estimate a treatment effect using observational data.

There are many different types of observational studies, and the best type of study to use in any given situation depends on the specific data and circumstances. Some common types of observational studies include case-control studies, cohort studies, and cross-sectional studies.

In a case-control study, the cases are individuals who have the outcome of interest, and the controls are individuals who do not have the outcome of interest. The cases and controls are then compared to see if there is a difference in the exposure to the treatment or exposure of interest.

In a cohort study, the participants are followed over time to see if there is a difference in the outcome of interest between those who are exposed to the treatment or exposure and those who are not exposed.

In a cross-sectional study, the participants are asked about their exposure to the treatment or exposure and their outcome of interest at the same time.

All of these types of studies can be used to estimate the effect of a treatment or exposure on an outcome of interest.

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Assignment Activity 8: Carry out sensitivity analyses for assumptions underlying estimating causal effects.

Sensitivity analysis is a type of statistical analysis that is used to examine how the results of a study would change if the assumptions underlying the study were not true. Sensitivity analysis can be used to assess the robustness of the results of a study to violations of the assumptions.

There are many different types of sensitivity analyses, and the best type of analysis to use in any given situation depends on the specific data and circumstances. Some common types of sensitivity analyses include univariate sensitivity analysis, multivariate sensitivity analysis, and regression discontinuity design.

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The assignment sample discussed above is based on ST4020 Causal Inference. This sample is just for reference and you should not use it as your final submission. You can also see ST313 Applied Regression Models assignment sample NUIG, ST314 Introduction to Biostatistics assignment sample NUIGCSOC10010 Introduction to Computational Social Science assignment sample, and many more samples on our website.

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