Efficiently Disentangle Causal Representations

Install dependency

pip install -r requirements.txt

Main experiments

Causality direction prediction

cd experiments/direction_prediction
python baseline.py
python proposed.py
python plots.py

Representation learning

cd experiments/representation_learning
python3 baseline.py
python3 proposed.py
python3 plots.py

Discussion experiments

Other metrics

cd experiments/other_metrics
python kl_divergence.py
python grad_l2_norm.py

Robustness

cd experiments/robustness
python baseline.py
python proposed.py

Temperature and altitude data

cd experiments/altitude_temperature
python altitude_temperature.py

Network architecture

cd experiments/altitude_temperature
python altitude_temperature.py --hidden_layers 2

Noise

cd experiments/altitude_temperature
python altitude_temperature.py --noise 1.0

Appendix experiments

Causality direction prediction with N=100

cd experiments/direction_prediction/
python baseline.py --N 100
python proposed.py --N 100
python plots_N=100.py

Causality direction prediction with continuous variable

cd experiments/direction_prediction_continuous
python baseline.py
python proposed.py

Robustness and edge cases

# Robustness analysis for the proposed method.
python example.py --experiment_type analysis

# An example of edge cases.
python example.py --experiment_type example