This guide outlines a six-step process for performing causal inference in R, covering data preparation, method selection (including regression, PSM, IV, RDD), implementation, assumption checking, result interpretation, and visualization. It details R packages for each step, provides illustrative code examples, and emphasizes the importance of critical assessment and transparent reporting of findings.

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Step Description R Packages Code Example (Illustrative)
1. Data Preparation and Exploration
Begin by importing your data into R. Thoroughly explore your data using summary statistics, visualizations (histograms, scatter plots, box plots), and correlation matrices to understand the relationships between your variables. Identify potential confounders and mediators. Check for missing data and handle it appropriately (imputation or removal).
 tidyverse (readr, ggplot2, dplyr), mice (for imputation)
library(tidyverse)
data <- read_csv("your_data.csv")
summary(data)
ggplot(data, aes(x = treatment, y = outcome)) + geom_boxplot()
2. Choosing a Causal Inference Method
Select an appropriate causal inference method based on your research question and data structure. Common methods include:
  • Regression analysis: Suitable for adjusting for confounders. Linear regression for continuous outcomes, logistic regression for binary outcomes.
  • Propensity score matching (PSM): Matches treated and control units based on their propensity to receive treatment. Useful when randomization is not feasible.
  • Instrumental variables (IV): Used when there's unobserved confounding and an instrument is available (a variable that affects treatment but not the outcome directly).
  • Regression discontinuity design (RDD): Exploits a discontinuity in treatment assignment based on a running variable.
  • Causal inference packages: Utilize packages that offer advanced methods, such as Bayesian approaches.
 lm, glm, MatchIt, ivreg, rdd, broom (for tidy output)
model <- lm(outcome ~ treatment + confounder1 + confounder2, data = data)
3. Implementing the Chosen Method in R
Use the appropriate R functions and packages to implement your chosen method. Carefully specify your model, including the treatment variable, outcome variable, and any covariates (confounders).
 See above for package suggestions based on the chosen method.
library(MatchIt)
matched_data <- matchit(treatment ~ confounder1 + confounder2, data = data, method = "nearest")
matched_data <- match.data(matched_data)
4. Assessing Model Assumptions and Diagnostics
Critically evaluate the assumptions of your chosen method. For example, in regression, check for linearity, homoscedasticity, and normality of residuals. For PSM, assess balance in covariates after matching. Consider sensitivity analysis to evaluate robustness to violations of assumptions.
 plot, summary, coeftest (from lmtest), bal.tab (from cobalt)
plot(model)
summary(matched_data)
5. Interpreting Results and Reporting
Interpret the results of your causal inference analysis. Report the estimated causal effect (e.g., treatment effect) and its statistical significance. Clearly state any limitations of your analysis and potential biases.
 broom, stargazer (for creating publication-ready tables)
tidy(model)
stargazer(model, type = "html")
6. Visualization and Communication
Visualize your results using appropriate plots (e.g., forest plots, causal diagrams) to aid understanding and communication. Create clear and concise tables to present your findings.


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