F6 Directed acyclic graphs and potential outcomes causal model Flashcards

1
Q

What is a DAG?

A

Graphical representation of the theorized data generating proces. It models chain of causal effects

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2
Q

Why use a DAG?

A

Simplifies theoretical arguments
Model the argument
Communication to the reader

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3
Q

What does nodes, arrows, filled and dotted lines mean?

A

Node: A random variable
Arrow: A causal relationship
Filled line: Observed
Dotted line: Unobserved

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4
Q

What are the principles of a DAG?

A

Causality runs in one direction forward in time (no cycles and no endogeneity)

Reverse causality and simultaneity are not possible

Causality is understood in terms of counterfactuals

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5
Q

What is a confounder? (draw it)

A

Affect both D and Y = an open backdoor path that needs to be controlled for.

D <– X –> Y

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6
Q

What is a collider? (draw it)

A

D and Y affect X = closed backdoor path. Controlling will result in bias.

D –> X <– Y

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7
Q

What is Jan’s strategy regarding colliders and confounders?

A

Include everything and hope direction of causality is the same

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8
Q

What is Y^1 and Y^0. What does Y_i mean?

A

Y^1: Treated group
Y^0: Untreated group
Y_i: Specific unit

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9
Q

What is ATE, ATT and ATU?

A

Theoretical quantities.

ATE: Average treatment effect. E[delta_i] = E[Y_i^1]-E[Y_i^0]. How the entire population respond if treated.

ATT: Average treatment effect on the treated. E[delta_i|D_i=1].

ATU: Average treatment effect on the untreated. E[delta_i|D_i=0]. What is the treatment effect for the control group if they were treated.

If succesful randomization, then ATE = ATT = ATU.

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10
Q

How can ATE be estimated?

A

Different from the true ATE (unknown) because of non-random selection bias.

We need some sort of random chock so that control and treatment group are similar on confounders.

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11
Q

What is an estimator?

A

A mathematical rule that we apply to arrive at a specific value of interest (illustrated with a hat).

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12
Q

What is three useful qualities for the beta koefficient?

A

It is unbiased, efficient and consistent

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13
Q

What is an unbiased estimator?

A

It’s centered on the true population parameter (can be biased due to confounder).

E(x-bar) = my

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14
Q

What is an efficient estimator?

A

The varians around the mean is low (likely that an estimate is close to the true population parameter)

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15
Q

What is a consistent estimator?

A

When sample size increases the estimator must converge to the true population parameter.

x-bar - my –> 0 as n –> ∞ (Law of large numbers)

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16
Q

What is the difference between standard deviation and standard error?

A

Both measure the standard deviation but in to different distributions.

Standard error: Distribution of coefficient
Standard deviation: Distribution of variable (sigma)

17
Q

What is SUTVA?

A

Stable unit treatment value assumption:

1) Homogenenous doses to all
2) No externalities (no spill over/interference between units)

No spill over to general equilibrium (scaling up)

18
Q

What is causal inference?

A

The study of counterfactuals / comparing counterfactuals

19
Q

What is the synonym for identification strategy?

A

What assumptions allow you to claim, that you have estimated a causal effect

20
Q

Can we estimate an individual causal effect?

A

No. The best we can do is average treatment effects

21
Q

What is selection bias?

A

Difference in outcome between treated and untreated if NO ONE was treated.

If randomization then E(Y_i^0|D=1)-E(Y_i^0|D=0) = 0

22
Q

When should we distinguish between ATT and ATE?

A

When we don’t have a randomized experiment, the ATE and ATT could be very different numerically.

23
Q

How are the assumptions for ATT different than ATE?

A

We observe Y^1 for the treated and to identify the ATT, we “only” have to find a control group that looks like the treatment group had they not been treated (the missing potential outcome Y^0).

We don’t need to assume Y^1 is the same for groups. We don’t need common support.

24
Q

What is causal inference, identification strategy and identifying assumption?

A

Causal inference: Thinking of counterfactuals

Strategy: Research design

Assumptions: Key assumption need to met for causal estimation.

25
Q

What is the fundamental problem of causality? What is the switching equation and how is the individual treatment effect estimated?

A

You can never observe both potential outcomes.

Y_i = D_iY_i^1 + (1-D_i)Y_i^0

Can never be estimated but: delta_i = Y_i^1 - Y_i^0