Causal Inference Methods for Policy Evaluation

01-Potential Outcome Causal Model

Jacopo Mazza

Utrecht School of Economics

2026

What is Policy Evaluation?

Policy Evaluation is:

The process of assessing the effects of a policy

  • What is the effect of minimum wage on employment?
  • What is the effect of a tax on carbon emissions on the environment?
  • What is the effect of lockdown on the spread of COVID-19?
  • What is the effect of parental leave on fertility?
  • What is the effect of R&D subsidies on innovation?

How do you evaluate a policy?

The Affordable Rent Act

  • Background.
    • In 2024 the Dutch government introduced the Affordable Rent Act to make renting more affordable.
    • Mechanism: A points system sets maximum rents based on property characteristics.
    • Key Thresholds:
      1. ≤ 186 points: Max €1,157.95/month
      2. ≤ 143 points: Max €880/month

      3. 186 points: Free market (no caps).

    • Criticism: It allegedly reduced rental supply and increased prices.

Key Questions

  • Did the law cause these unintended consequences?
  • How can we determine the causal impact of the policy?

Simple: look at the data!

Simple: look at the data!

Simple: look at the data!

So what is causation?

So why is correlation not causation?

  1. Reverse causality: the effect causes the cause
  1. Omitted variable bias: the cause and the effect are caused by a third variable
  1. Simultaneity: the cause and the effect are jointly determined
  1. Selection bias: the cause is not randomly assigned

Understanding causation: the counterfactual

“If a person eats of a particular dish, and dies in consequence, that is, would not have died if he had not eaten of it, then the food was the cause of his death.”

— J.S. Mill

The counterfactual in policy evaluation

Can you think of a policy impacted by false assumptions about causality?

The Potential Outcome Model

The counterfactual language

A unit (\(i\)) (e.g. an individual, a firm, a country) can be:

  • treated (\(D_i = 1\))
  • untreated (\(D_i = 0\))

We indicate:

  • the potential outcome of unit \(i\) under treatment as \(Y_i^1\)
  • the potential outcome of unit \(i\) under no treatment as \(Y_i^0\)
  • the observed outcome of unit \(i\) as \(Y_i\)

The switching equation

The observed outcome of unit \(i\) is: \[ Y_i = D_i Y_i^1 + (1 - D_i) Y_i^0 \]

  • if \(D_i = 1\) then \(Y_i = Y_i^1\) and \(Y_i^0\) is the counterfactual
  • if \(D_i = 0\) then \(Y_i = Y_i^0\) and \(Y_i^1\) is the counterfactual

The treatment effect of unit \(i\) is: \[ \delta_i = Y_i^1 - Y_i^0 \]

…but we can only observe one of the two potential outcomes!

The fundamental problem of causal inference

Important

\(\delta_i\) can never be observed

  • \(\delta_i\) is fundamentally unobservable
  • \(\delta_i\) is not the same as the observed difference between groups
  • Causal inference is fundamentally a problem of missing data

Can we not just compare means?

  • Observed mean of the treated units:
    \(Avg[Y_i|D_i=1] = \frac{1}{N_1} \sum_{i=1}^{N_1} Y_i\)
  • Observed mean of the untreated units:
    \(Avg[Y_i|D_i=0] = \frac{1}{N_0} \sum_{i=1}^{N_0} Y_i\)
  • Observed difference:
    \(Avg[Y_i|D_i=1] - Avg[Y_i|D_i=0] = \\ Avg[Y_i^1|D_i=1] - Avg[Y_i^0|D_i=0]\)
  • Remember: \(\delta_i = Y_i^1 - Y_i^0\)
  • We can show that: \[ Avg_n[Y_i^1|D_i=1] - Avg_n[Y_i^0|D_i=0] = \\ {\delta + Avg_n[Y_i^0|D_i=1] - Avg_n[Y_i^0|D_i=0]} \]

The outcome of cancer treatment

Patient \(Y^1\) \(Y^0\) \(\delta\)
1 7 1 6
2 5 6 -1
3 5 1 4
4 7 8 -1
5 4 2 2
6 10 1 9
7 1 10 -9
8 5 6 -1
9 3 7 -4
10 9 8 1

The outcome of cancer treatment

Patient \(Y^1\) \(Y^0\) \(\delta\)
1 7 1 6
2 5 6 -1
3 5 1 4
4 7 8 -1
5 4 2 2
6 10 1 9
7 1 10 -9
8 5 6 -1
9 3 7 -4
10 9 8 1
  • \(Y^1\) is the years gained with surgery
  • \(Y^0\) is the years gained with chemotherapy
  • \(E[Y^1] = 5.6\) and \(E[Y^0] = 5\)
  • \(\delta\) is the causal effect of surgery on years gained
  • \(E[\delta] = E[Y^1]-E[Y^0]= 0.6\)
  • In this case \(\delta\) is the average treatment effect (ATE)

The outcome of cancer treatment

Patient \(Y^1\) \(Y^0\) \(\delta\) D
1 7 1 6 1
2 5 6 -1 0
3 5 1 4 1
4 7 8 -1 0
5 4 2 2 1
6 10 1 9 1
7 1 10 -9 0
8 5 6 -1 0
9 3 7 -4 0
10 9 8 1 1

The outcome of cancer treatment

Patient \(Y^1\) \(Y^0\) \(\delta\) D
1 7 6 1
2 6 -1 0
3 5 4 1
4 8 -1 0
5 4 2 1
6 10 9 1
7 10 -9 0
8 6 -1 0
9 7 -4 0
10 9 1 1
  • \(D_i=1\) if patient \(i\) is treated with surgery
  • \(D_i=0\) if patient \(i\) is treated with chemotherapy
  • The perfect doctor will always choose the best treatment
  • The observed outcome is \(Y_i = D_i Y_i^1 + (1 - D_i) Y_i^0\)
  • If I take the difference in means I get:
    \(Avg[Y_i^1|D_i=1] - Avg[Y_i^0|D_i=0] = \\ 7 - 7.4 = -0.4\)

ATE or Nothing?

  • ATE is probably the most natural measure of treatment effect
  • But it is not the only one:

    1. Average Treatment Effect on the Treated (ATT):
      \(E[Y_i^1 - Y_i^0|D_i=1]\)

    2. Average Treatment Effect on the Untreated (ATU):
      \(E[Y_i^1 - Y_i^0|D_i=0]\)

    3. Local Average Treatment Effect (LATE)

    4. Intention to Treat (ITT)

  • What we can estimate depends on the data available
  • What we care for depends on the question we want to answer