Causal Inference in DotA 2 when estimated through randomized data

Causal Inference in DotA 2 when estimated through randomized data

Avgousti, Stelios (TU Delft Electrical Engineering, Mathematics and Computer Science)

Krijthe, J.H. (mentor)
Karlsson, R.K.A. (mentor)
Hildebrandt, K.A. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-24

Strategy games could be considered as an amazing playground for using Causal inference methods. The complex nature of the data and the built-in randomization help with testing causal inference in a scenario where in reality it would be hard and expensive. Randomized data in coherence with causal inference is well documented and tested, but not regarding the strategy game of interest DotA 2. To evaluate the quality of causal inference using randomized data for predictions in the game, the average causal effect estimand is used. The calculation of the average causal effect of certain events between different intervals and their comparison in addition to the calculation of the statistical Independence between variables of concern comprise the bulk of the research. The calculations allow for logical deductions and statistical correlations between values to reach a conclusion. The final verdict being that causal inference with randomized data is helpful for predicting events in DotA 2 but the amount of data and existing complex biases can be deceiving and can heavily influence the results.

Causal Inference
Randomization
DotA 2

http://resolver.tudelft.nl/uuid:f23a7610-448d-4898-ac72-e576e5f4d74a

https://github.com/stelios34S/Causal-inference-in-DotA-2-when-estimated-through-randomized-data.git Source Code Repository

Student theses

bachelor thesis

© 2022 Stelios Avgousti