Week 5 - PS Matching Flashcards

1
Q

What is the difference between the common support requirements for matching for estimating the ATT and the ATE?

A

Estimating the ATT only requires the distribution of propensity scores for the treated is contained within the distribution of the untreated. ATE needs common support for both treated and control.

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2
Q

Why is it advantageous to match based on the logit of the propensity score rather than the propensity score itself?

A

Doing so avoids compression around zero and one.

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3
Q

How is greedy matching performed?

A

Choose a treated case and search for the best available match among the untreated cases without accounting for the quality of the match of the entire treated sample.

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4
Q

How is optimal matching performed?

A

Optimal matching uses a network flow optimization problem to minimize total weighted sample distance, thereby selecting the “optimal” match.

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5
Q

How is genetic matching performed?

A

Genetic matching uses a genetic search algorithm to find a set of weights for each covariate such that a version of optimal balance is achieved after matching.

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6
Q

What is the role of the generalized Mahalanobis distance in genetic matching?

A

The distance minimized by the genetic matching algorithm is the generalized Mahalanobis distance (GMD).

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7
Q

In what situation would greedy matching be expected to perform as well as optimal matching?

A

In situations when there are large numbers of control units available for matching to treated units.

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8
Q

What is the advantage of optimal matching over greedy matching?

A

Optimal matching performs as well or better than greedy matching with respect to minimum distance and balance.

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9
Q

What is the advantage of genetic matching over optimal and greedy matching?

A

Genetic matching attempts to minimize a global distance between the matches on all covariates.

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10
Q

What is the expected difference in performance between one-to-one, one-to-many, and variable ratio matching?

A

Greater bias reduction. One-to-many (never use), because you have to sacrifice quality to maintain the strict number assignment. One-to-one and variable ratio provide good quality matches.

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11
Q

How is a caliper used in matching and what is the advantage of using it?

A

Maximum distance to allow matches, and if no match is found within the distance the treated observation is dropped for lack of common support. It forces common support, and match quality.

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12
Q

How can common support be strictly enforced in propensity score matching?

A

Through the use of a caliper.

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13
Q

What is the expected difference in performance between matching with and without replacement?

A

Matching with replacement has the advantage of always matching each treated unit to the closest untreated unit, and therefore produces larger bias reduction than matching without replacement.

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14
Q

How is full matching performed?

A

Each treated unit is matched to at least one control unit and vice versa, without replacement. This procedure can be viewed as a propensity score stratification where the number of strata containing at least one treated and one untreated observation is maximized.

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15
Q

Which matching methods require the use of weights and why?

A

Genetic AND optimal matching… to optimize post-matching covariate balance.

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16
Q

Why do some researchers argue that matched samples should be treated as independent samples?

A

Because matching does not produce correlations between outcomes of matched individuals.

17
Q

Why do some researchers argue that matched samples should be treated as related samples?

A

Because covariates which have similar distributions for matched and treated groups are related to outcomes, the distributions of outcomes will be more similar for treated and matched samples than from randomly selected samples.

18
Q

What is the Abadie and Imbens simple estimator for matched samples?

A

SEE PAGE 12 IN THE CHAPTER!!!

19
Q

How can treatment effects for matched samples be estimated with regression?

A

with or without additional covariate adjustment…
Y-hat sub m = alpha-hat + swirly T (treatment effect)-hat times Z sub-m
Y-hat sub m = alpha-hat + swirly T (treatment effect)-hat times Z sub-m + X sub-m times beta-hat

20
Q

What is the objective of sensitivity analysis?

A

The objective of sensitivity analysis is to determine the magnitude to which hidden bias would change inferences about a treatment effect.

21
Q

What is the role of the odds ratio and the gamma constant in Rosenbaum’s sensitivity analysis?

A

If there is hidden bias, the odds ratio will be larger than one and smaller than a constant GAMMA.

A study is sensitive to hidden bias if values of GAMMA  lead to change in inferences.