Causal Inference Methods Overview
causal-methods-overview.Rmd🧠 What is Causal Inference?
Causal inference is the process of estimating the effect of an intervention or treatment on an outcome. Unlike correlation, causal inference requires assumptions about how data was generated.
In this article, we use the fund_performance dataset to
illustrate common methods.
## fund_id market_return alpha beta treatment return
## 1 1 0.003952435 -0.009915974 0.9217857 0 -0.007775751
## 2 2 0.036982251 -0.010799101 1.1331275 1 0.032828934
## 3 3 0.215870831 0.009640395 1.0374590 1 0.224115880
## 4 4 0.067050839 0.007356497 1.1228787 1 0.080673608
## 5 5 0.072928774 -0.040986855 0.9176203 0 0.051918971
## 6 6 0.231506499 0.030811469 0.8564341 0 0.228707376⚖️ 1. Naive Difference in Means
This method assumes random treatment assignment.
fund_performance %>%
group_by(treatment) %>%
summarise(mean_return = mean(return))## # A tibble: 2 × 2
## treatment mean_return
## <int> <dbl>
## 1 0 0.0724
## 2 1 0.0764🎯 2. Propensity Score Matching (PSM)
PSM attempts to create a balanced dataset by matching treated and control units on their probability of receiving treatment.
psm <- matchit(treatment ~ market_return + alpha + beta, data = fund_performance, method = "nearest")
summary(psm)##
## Call:
## matchit(formula = treatment ~ market_return + alpha + beta, data = fund_performance,
## method = "nearest")
##
## Summary of Balance for All Data:
## Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
## distance 0.4942 0.4919 0.0961 0.9914 0.0250
## market_return 0.0623 0.0610 0.0134 0.9360 0.0079
## alpha 0.0102 0.0115 -0.0687 0.9442 0.0194
## beta 0.9919 0.9993 -0.0654 0.9965 0.0186
## eCDF Max
## distance 0.0725
## market_return 0.0232
## alpha 0.0512
## beta 0.0413
##
## Summary of Balance for Matched Data:
## Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
## distance 0.4942 0.4920 0.0887 1.0413 0.0230
## market_return 0.0623 0.0602 0.0208 0.9314 0.0088
## alpha 0.0102 0.0113 -0.0568 0.9830 0.0167
## beta 0.9919 0.9992 -0.0650 1.0060 0.0185
## eCDF Max Std. Pair Dist.
## distance 0.0730 0.0927
## market_return 0.0243 1.1393
## alpha 0.0467 0.8110
## beta 0.0426 0.8197
##
## Sample Sizes:
## Control Treated
## All 507 493
## Matched 493 493
## Unmatched 14 0
## Discarded 0 0Check matched data:
matched_data <- match.data(psm)
ggplot(matched_data, aes(x = return, fill = factor(treatment)))
geom_density(alpha = 0.5)## geom_density: na.rm = FALSE, orientation = NA, outline.type = upper
## stat_density: na.rm = FALSE, orientation = NA
## position_identity
labs(title = "Return Distribution After Matching", fill = "Treatment")## $fill
## [1] "Treatment"
##
## $title
## [1] "Return Distribution After Matching"
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## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE🌲 3. Causal Forests (Optional)
You can also use more advanced estimators like causal
forests (via the grf package). These are not
covered here but can estimate heterogeneous treatment effects.
Install and explore here: https://grf-labs.github.io/grf/
📌 Summary
| Method | When to Use |
|---|---|
| Naive Difference-in-Means | Randomized experiments |
| Propensity Score Matching | Observational studies with covariate overlap |
| Causal Forests | Heterogeneous treatment effects |
📖 Citation
citation("CausalInvestData")## To cite the CausalInvestData package in publications, use:
##
## Conilias Zvobwo E (2025). _CausalInvestData: Simulated Datasets for
## Causal Inference in Investment Management_. R package version 0.1.0,
## <https://github.com/edzai/CausalInvestData>.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {CausalInvestData: Simulated Datasets for Causal Inference in Investment Management},
## author = {Edzai {Conilias Zvobwo}},
## year = {2025},
## note = {R package version 0.1.0},
## url = {https://github.com/edzai/CausalInvestData},
## }