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# utils/do_calculus.py
import pandas as pd
import numpy as np
import networkx as nx
class DoCalculus:
def __init__(self, graph):
self.graph = graph
def intervene(self, data, intervention_var, intervention_value):
"""
Simulate do(X=x) intervention on a variable.
Returns intervened DataFrame.
This is a simplified implementation.
"""
intervened_data = data.copy()
# Direct intervention: set the value
intervened_data[intervention_var] = intervention_value
# Propagate effects (simplified linear model) - needs graph
# For a true do-calculus, you'd prune the graph and re-estimate based on parents
# For demonstration, this still uses a simplified propagation.
try:
# Ensure graph is connected and topological sort is possible
if self.graph and not nx.is_directed_acyclic_graph(self.graph):
print("Warning: Graph is not a DAG. Topological sort may fail or be incorrect for do-calculus.")
# This simplified propagation is a conceptual placeholder
for node in nx.topological_sort(self.graph):
if node == intervention_var:
continue # Do not propagate back to the intervened variable
parents = list(self.graph.predecessors(node))
if parents:
# Very simplified linear model to show propagation
# In reality, you'd use learned coefficients or structural equations
combined_effect = np.zeros(len(intervened_data))
for p in parents:
if p in intervened_data.columns:
# Use a fixed random coefficient for demonstration
coeff = 0.5
combined_effect += intervened_data[p].to_numpy() * coeff
# Add a small random noise to simulate uncertainty
intervened_data[node] += combined_effect + np.random.normal(0, 0.1, len(intervened_data))
except Exception as e:
print(f"Could not perform full propagation due to graph issues or simplification: {e}")
# Fallback to direct intervention only if graph logic fails
pass # The direct intervention `intervened_data[intervention_var] = intervention_value` is already applied
return intervened_data |