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1/ 🔥 Speeding Up Your R Code: Profiling, Benchmarking, and Parallelization 🚀 Ever felt like your R code could run faster? In this thread, we'll explore advanced techniques to optimize and parallelize your code. Stay tuned! #RStats #AdvancedR #DataScience 
2/ 🕵️♂️ Profiling: Before optimizing, you need to know where your code bottlenecks are. Use R's built-in tools like Rprof() and summaryRprof() or the profvis package to visualize and analyze your code's performance. #profiling #RStats #DataScience
3/ ⏱️ Benchmarking: Compare different implementations of a function or solution using the microbenchmark or bench package. This will help you choose the most efficient approach. #benchmarking #RStats #DataScience
Out today is our paper on estimating causal effects on a network! This was joint work with twitterless Eric Tchetgen Tchetgen and Ilya Shpitser.
This project occupied a significant portion of my PhD… and about 10% of my life… so enjoy!
tandfonline.com/doi/full/10.10…
1/n
This project occupied a significant portion of my PhD… and about 10% of my life… so enjoy!
tandfonline.com/doi/full/10.10…
1/n
We wanted a method to estimate causal effects in complex settings:
- The outcome of one person (1) is influenced by others’ outcomes (2) depends on others’ exposures
- Persons cannot be divided into distinct clusters (“network hairball”)
- The exposure is not randomized
2/n
- The outcome of one person (1) is influenced by others’ outcomes (2) depends on others’ exposures
- Persons cannot be divided into distinct clusters (“network hairball”)
- The exposure is not randomized
2/n
Specifically, we were interested in the expected change in a person’s outcome corresponding to a hypothetical change in:
their exposure, while the exposures of other persons stay the same (direct)
other persons’ exposures, while their exposure stays the same (spillover)
3/n
their exposure, while the exposures of other persons stay the same (direct)
other persons’ exposures, while their exposure stays the same (spillover)
3/n
