Yes. In fact, causal modelling is one of the most advanced topics in quantitative finance and is becoming increasingly important because traditional ML models (LSTM, XGBoost, Transformers) often learn correlations, whereas causal models aim to discover why prices move.
Since I am pursuing an MSc in Financial Engineering at WQU, I'll teach it at that level, covering theory, mathematics, and Python implementations.
Learning Roadmap
We'll build from scratch.
Correlation vs Causation
Structural Causal Models (SCM)
Directed Acyclic Graphs (DAGs)
Causal Discovery Algorithms
Do-Calculus (Pearl)
Counterfactual Prediction
Causal Forecasting
Applying causal models to stock prediction
Building an end-to-end project
Step 1 — Correlation vs Causation
Suppose we have
| Oil Price | Airline Stock |
|---|---|
| ↑ | ↓ |
| ↓ | ↑ |
They are negatively correlated.
But does oil directly affect airlines?
Yes.
Higher fuel cost
→ higher operating expenses
→ lower profit
→ lower stock price
This is a causal relationship.
Now another example.
| Ice Cream Sales | Shark Attacks |
|---|---|
| ↑ | ↑ |
Highly correlated.
Does ice cream cause sharks?
No.
Common cause:
Summer
→ more people swim
→ more shark attacks
Summer
→ more ice cream sales
This is a confounder.
Traditional ML usually cannot distinguish these.
Step 2 — Financial Example
Imagine predicting Apple stock.
Variables
Interest Rate
Inflation
USD Index
Nasdaq Index
Apple Earnings
Apple Stock
The causal graph looks like
Interest Rate
\
\
\
Inflation ---> USD
\
\
\
Nasdaq ----\
\
\
Apple Earnings
\
\
Apple Stock
Notice
Interest rate does NOT directly cause Apple stock.
It changes
Interest Rate
↓
Bond Yield
↓
Investor Preference
↓
Nasdaq
↓
Apple
That matters.
Step 3 — Structural Causal Model (SCM)
Every node has an equation.
Example
Inflation = Noise
InterestRate = 0.8 × Inflation + Noise
USD = 0.5 × InterestRate + Noise
Nasdaq = -0.7 × InterestRate + Noise
Apple =
0.6 × Nasdaq
+0.4 × Earnings
+Noise
Unlike regression,
each equation represents a mechanism.
Python Example
Generate synthetic causal data.
import numpy as np
import pandas as pd
np.random.seed(42)
N = 1000
inflation = np.random.normal(2,0.5,N)
interest = 0.8*inflation + np.random.normal(0,0.2,N)
usd = 0.5*interest + np.random.normal(0,0.3,N)
nasdaq = -0.7*interest + np.random.normal(0,0.5,N)
earnings = np.random.normal(5,1,N)
apple = (
0.6*nasdaq
+0.4*earnings
+np.random.normal(0,0.5,N)
)
df = pd.DataFrame({
"Inflation":inflation,
"Interest":interest,
"USD":usd,
"Nasdaq":nasdaq,
"Earnings":earnings,
"Apple":apple
})
print(df.head())
Notice that we explicitly generated the causal relationships.
Step 4 — Draw the DAG
Using NetworkX
import networkx as nx
import matplotlib.pyplot as plt
G = nx.DiGraph()
G.add_edges_from([
("Inflation","Interest"),
("Interest","USD"),
("Interest","Nasdaq"),
("Nasdaq","Apple"),
("Earnings","Apple")
])
nx.draw(G,
with_labels=True,
node_size=3000,
arrows=True)
plt.show()
Produces
Inflation
↓
Interest
↙ ↘
USD Nasdaq
↓
Apple
↑
Earnings
Step 5 — Why This Is Better Than Regression
Suppose Fed raises rates.
Regression says
Past data:
Rate ↑
Apple ↓
Therefore predict Apple ↓
But suppose
Apple reports record earnings.
Causal model says
Interest ↑
↓
Nasdaq ↓
↓
Apple ↓
BUT
Earnings ↑↑
↓
Apple ↑
The model understands competing causes.
This is much closer to how human analysts reason.
Step 6 — Counterfactual Prediction
Question:
"What would Apple have done if the Fed had NOT increased rates?"
Regression cannot answer.
Causal model can.
interest = 5.5
# Intervention
interest = 2.0
# Recompute downstream variables
usd = 0.5*interest
nasdaq = -0.7*interest
apple = 0.6*nasdaq + 0.4*earnings
This is called an intervention:
do(Interest=2%)
instead of merely observing Interest = 2%.
Step 7 — Real Stock Prediction
For NIFTY prediction, a richer causal graph might be:
Fed Rate
↓
US Dollar
↓
Crude Oil
↓
Indian Inflation
↓
RBI Rate
↓
Bank Nifty
↓
NIFTY
GDP
↓
Corporate Earnings
↓
NIFTY
VIX
↓
NIFTY
FII Flow
↓
NIFTY
A causal model uses these relationships rather than feeding all variables into a black-box predictor.
Step 8 — Libraries
Popular Python libraries include:
DoWhy— causal inference and effect estimationCausalNex— Bayesian networks and causal graphspgmpy— Bayesian networks (already familiar from your coursework)econml— heterogeneous treatment effectslingam— causal discovery for linear non-Gaussian modelscausal-learn— PC, GES, FCI, NOTEARS and other discovery algorithmsnetworkx— graph visualization
Example with DoWhy:
from dowhy import CausalModel
model = CausalModel(
data=df,
treatment="Interest",
outcome="Apple",
common_causes=["Inflation"]
)
identified_estimand = model.identify_effect()
estimate = model.estimate_effect(
identified_estimand,
method_name="backdoor.linear_regression"
)
print(estimate.value)
Final Project (MSc-Level)
A strong capstone could combine:
Data collection
Yahoo Finance
FRED (interest rates)
RBI
FII/DII flows
VIX
Crude oil
USD/INR
CPI
Causal discovery
PC Algorithm
NOTEARS
LiNGAM
Bayesian Network
Build the DAG
Learn conditional probability tables or continuous conditional distributions
Causal forecasting
Estimate intervention effects
Perform counterfactual analysis
Forecast index or stock prices under different macroeconomic scenarios
Explainability
Show which causal paths contributed most to the prediction
Compare against LSTM, XGBoost, and Transformer models
This kind of project aligns well with your financial engineering background because it integrates probabilistic graphical models, macroeconomic reasoning, and interpretable AI rather than relying solely on black-box prediction.
